Feature: Filter k most confident masks (#720)

* add filter to things that return confidences need to add ui elements to select between the two methods

* add ui elements for controlling method

* forgot to remove this

* fix incorrect early exit

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* fix: pop mask only top k params

* fix: filter confidences

* refactor: change to one public function

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Dowon <[email protected]>

aaaaaa/ui.py

@@ -294,14 +294,21 @@ 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),
+            w.ad_mask_filter_method = gr.Radio(
+                choices=["Area", "Confidence"],
+                value="Area",
+                label="Method to filter top k masks by (confidence or area)",
+                visible=True,
+                elem_id=eid("ad_mask_filter_method"),
+            )
+            w.ad_mask_k = gr.Slider(
+                label="Mask only the top k (0 to disable)" + suffix(n),
                 minimum=0,
                 maximum=10,
                 step=1,
                 value=0,
                 visible=True,
-                elem_id=eid("ad_mask_k_largest"),
+                elem_id=eid("ad_mask_k"),
             )
 
         with gr.Column(variant="compact"):

adetailer/args.py

@@ -60,7 +60,8 @@ class ADetailerArgs(BaseModel, extra=Extra.forbid):
     ad_prompt: str = ""
     ad_negative_prompt: str = ""
     ad_confidence: confloat(ge=0.0, le=1.0) = 0.3
-    ad_mask_k_largest: NonNegativeInt = 0
+    ad_mask_filter_method: Literal["Area", "Confidence"] = "Area"
+    ad_mask_k: NonNegativeInt = 0
     ad_mask_min_ratio: confloat(ge=0.0, le=1.0) = 0.0
     ad_mask_max_ratio: confloat(ge=0.0, le=1.0) = 1.0
     ad_dilate_erode: int = 4
@@ -131,7 +132,11 @@ def extra_params(self, suffix: str = "") -> dict[str, Any]:
         ppop("ADetailer prompt")
         ppop("ADetailer negative prompt")
         p.pop("ADetailer tab enable", None)  # always pop
-        ppop("ADetailer mask only top k largest", cond=0)
+        ppop(
+            "ADetailer mask only top k",
+            ["ADetailer mask only top k", "ADetailer method to decide top k masks"],
+            cond=0,
+        )
         ppop("ADetailer mask min ratio", cond=0.0)
         ppop("ADetailer mask max ratio", cond=1.0)
         ppop("ADetailer x offset", cond=0)
@@ -217,7 +222,8 @@ def need_skip(self) -> bool:
     ("ad_prompt", "ADetailer prompt"),
     ("ad_negative_prompt", "ADetailer negative prompt"),
     ("ad_confidence", "ADetailer confidence"),
-    ("ad_mask_k_largest", "ADetailer mask only top k largest"),
+    ("ad_mask_filter_method", "ADetailer method to decide top k masks"),
+    ("ad_mask_k", "ADetailer mask only top k"),
     ("ad_mask_min_ratio", "ADetailer mask min ratio"),
     ("ad_mask_max_ratio", "ADetailer mask max ratio"),
     ("ad_x_offset", "ADetailer x offset"),

adetailer/common.py

@@ -22,6 +22,7 @@
 class PredictOutput(Generic[T]):
     bboxes: list[list[T]] = field(default_factory=list)
     masks: list[Image.Image] = field(default_factory=list)
+    confidences: list[float] = field(default_factory=list)
     preview: Optional[Image.Image] = None
 
 

adetailer/mask.py

@@ -225,6 +225,7 @@ def filter_by_ratio(
     idx = [i for i in range(items) if is_in_ratio(pred.bboxes[i], low, high, orig_area)]
     pred.bboxes = [pred.bboxes[i] for i in idx]
     pred.masks = [pred.masks[i] for i in idx]
+    pred.confidences = [pred.confidences[i] for i in idx]
     return pred
 
 
@@ -236,9 +237,31 @@ def filter_k_largest(pred: PredictOutput[T], k: int = 0) -> PredictOutput[T]:
     idx = idx[::-1]
     pred.bboxes = [pred.bboxes[i] for i in idx]
     pred.masks = [pred.masks[i] for i in idx]
+    pred.confidences = [pred.confidences[i] for i in idx]
     return pred
 
 
+def filter_k_most_confident(pred: PredictOutput[T], k: int = 0) -> PredictOutput[T]:
+    if not pred.bboxes or not pred.confidences or k == 0:
+        return pred
+    idx = np.argsort(pred.confidences)[-k:]
+    idx = idx[::-1]
+    pred.bboxes = [pred.bboxes[i] for i in idx]
+    pred.masks = [pred.masks[i] for i in idx]
+    pred.confidences = [pred.confidences[i] for i in idx]
+    return pred
+
+
+def filter_k_by(
+    pred: PredictOutput[T], k: int = 0, by: str = "Area"
+) -> PredictOutput[T]:
+    if by == "Area":
+        return filter_k_largest(pred, k)
+    if by == "Confidence":
+        return filter_k_most_confident(pred, k)
+    raise RuntimeError
+
+
 # Merge / Invert
 def mask_merge(masks: list[Image.Image]) -> list[Image.Image]:
     arrs = [np.array(m) for m in masks]

adetailer/mediapipe.py

@@ -52,6 +52,7 @@ def mediapipe_face_detection(
     preview_array = img_array.copy()
 
     bboxes = []
+    confidences = []
     for detection in pred.detections:
         draw_util.draw_detection(preview_array, detection)
 
@@ -63,12 +64,15 @@ def mediapipe_face_detection(
         x2 = x1 + w
         y2 = y1 + h
 
+        confidences.append(detection.score)
         bboxes.append([x1, y1, x2, y2])
 
     masks = create_mask_from_bbox(bboxes, image.size)
     preview = Image.fromarray(preview_array)
 
-    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+    return PredictOutput(
+        bboxes=bboxes, masks=masks, confidences=confidences, preview=preview
+    )
 
 
 def mediapipe_face_mesh(
@@ -141,7 +145,6 @@ def mediapipe_face_mesh_eyes_only(
 
         preview = image.copy()
         masks = []
-
         for landmarks in pred.multi_face_landmarks:
             points = np.array(
                 [[land.x * w, land.y * h] for land in landmarks.landmark], dtype=int

adetailer/ultralytics.py

@@ -37,11 +37,16 @@ def ultralytics_predict(
         masks = create_mask_from_bbox(bboxes, image.size)
     else:
         masks = mask_to_pil(pred[0].masks.data, image.size)
+
+    confidences = pred[0].boxes.conf.cpu().numpy().tolist()
+
     preview = pred[0].plot()
     preview = cv2.cvtColor(preview, cv2.COLOR_BGR2RGB)
     preview = Image.fromarray(preview)
 
-    return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
+    return PredictOutput(
+        bboxes=bboxes, masks=masks, confidences=confidences, preview=preview
+    )
 
 
 def apply_classes(model: YOLO | YOLOWorld, model_path: str | Path, classes: str):

scripts/!adetailer.py

@@ -52,7 +52,7 @@
 from adetailer.common import PredictOutput, ensure_pil_image, safe_mkdir
 from adetailer.mask import (
     filter_by_ratio,
-    filter_k_largest,
+    filter_k_by,
     has_intersection,
     is_all_black,
     mask_preprocess,
@@ -596,7 +596,7 @@ def pred_preprocessing(self, p, pred: PredictOutput, args: ADetailerArgs):
         pred = filter_by_ratio(
             pred, low=args.ad_mask_min_ratio, high=args.ad_mask_max_ratio
         )
-        pred = filter_k_largest(pred, k=args.ad_mask_k_largest)
+        pred = filter_k_by(pred, k=args.ad_mask_k, by=args.ad_mask_filter_method)
         pred = self.sort_bboxes(pred)
         masks = mask_preprocess(
             pred.masks,