feat: Masking only the top k largest predictions (#264)

adetailer/args.py

@@ -70,6 +70,7 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
     ad_controlnet_weight: confloat(ge=0.0, le=1.0) = 1.0
     ad_controlnet_guidance_start: confloat(ge=0.0, le=1.0) = 0.0
     ad_controlnet_guidance_end: confloat(ge=0.0, le=1.0) = 1.0
+    ad_mask_k_largest: NonNegativeInt = 0
     is_api: bool = True
 
     @root_validator(skip_on_failure=True)
@@ -190,6 +191,7 @@ _all_args = [
     ("ad_confidence", "ADetailer confidence"),
     ("ad_mask_min_ratio", "ADetailer mask min ratio"),
     ("ad_mask_max_ratio", "ADetailer mask max ratio"),
+    ("ad_mask_k_largest", "ADetailer mask only top k largest"),
     ("ad_x_offset", "ADetailer x offset"),
     ("ad_y_offset", "ADetailer y offset"),
     ("ad_dilate_erode", "ADetailer dilate/erode"),

adetailer/mask.py

@@ -215,6 +215,16 @@ def filter_by_ratio(pred: PredictOutput, low: float, high: float) -> PredictOutp
     return pred
 
 
+def filter_take_largest(pred: PredictOutput, k: int) -> PredictOutput:
+    if not pred.bboxes or k == 0:
+        return pred
+    areas = [bbox_area(bbox) for bbox in pred.bboxes]
+    idx = np.argsort(areas)[-k:]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    return pred
+
+
 # Merge / Invert
 def mask_merge(masks: list[Image.Image]) -> list[Image.Image]:
     arrs = [np.array(m) for m in masks]

adetailer/ui.py

@@ -194,7 +194,7 @@ def detection(w: Widgets, n: int, is_img2img: bool):
     eid = partial(elem_id, n=n, is_img2img=is_img2img)
 
     with gr.Row():
-        with gr.Column():
+        with gr.Column(variant="compact"):
             w.ad_confidence = gr.Slider(
                 label="Detection model confidence threshold" + suffix(n),
                 minimum=0.0,
@@ -204,6 +204,15 @@ def detection(w: Widgets, n: int, is_img2img: bool):
                 visible=True,
                 elem_id=eid("ad_confidence"),
             )
+            w.ad_mask_k_largest = gr.Slider(
+                label="Mask only the top k largest (0 to disable)" + suffix(n),
+                minumum=0,
+                maximum=5,
+                step=1,
+                value=0,
+                visible=True,
+                elem_id=eid("ad_mask_k_largest")
+            )
 
         with gr.Column(variant="compact"):
             w.ad_mask_min_ratio = gr.Slider(
@@ -226,6 +235,7 @@ def detection(w: Widgets, n: int, is_img2img: bool):
             )
 
 
+
 def mask_preprocessing(w: Widgets, n: int, is_img2img: bool):
     eid = partial(elem_id, n=n, is_img2img=is_img2img)
 

scripts/!adetailer.py

@@ -26,7 +26,7 @@ from adetailer import (
 )
 from adetailer.args import ALL_ARGS, BBOX_SORTBY, ADetailerArgs, EnableChecker
 from adetailer.common import PredictOutput
-from adetailer.mask import filter_by_ratio, mask_preprocess, sort_bboxes
+from adetailer.mask import filter_take_largest, filter_by_ratio, mask_preprocess, sort_bboxes
 from adetailer.traceback import rich_traceback
 from adetailer.ui import adui, ordinal, suffix
 from controlnet_ext import ControlNetExt, controlnet_exists, get_cn_models
@@ -463,6 +463,7 @@ class AfterDetailerScript(scripts.Script):
         pred = filter_by_ratio(
             pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
         )
+        pred = filter_take_largest(pred, k=args.ad_mask_k_largest)
         pred = self.sort_bboxes(pred)
         return mask_preprocess(
             pred.masks,