feat: mediapipe face mesh

adetailer/common.py

@@ -13,7 +13,7 @@ repo_id = "Bingsu/adetailer"
 
 @dataclass
 class PredictOutput:
-    bboxes: list[list[float]] = field(default_factory=list)
+    bboxes: list[list[int | float]] = field(default_factory=list)
     masks: list[Image.Image] = field(default_factory=list)
     preview: Optional[Image.Image] = None
 
@@ -54,6 +54,7 @@ def get_models(
         {
             "mediapipe_face_full": None,
             "mediapipe_face_short": None,
+            "mediapipe_face_mesh": None,
         }
     )
 
@@ -94,3 +95,29 @@ def create_mask_from_bbox(
         mask_draw.rectangle(bbox, fill=255)
         masks.append(mask)
     return masks
+
+
+def create_bbox_from_mask(
+    masks: list[Image.Image], shape: tuple[int, int]
+) -> list[list[int]]:
+    """
+    Parameters
+    ----------
+        masks: list[Image.Image]
+            A list of masks
+        shape: tuple[int, int]
+            shape of the image (width, height)
+
+    Returns
+    -------
+        bboxes: list[list[float]]
+        A list of bounding boxes
+
+    """
+    bboxes = []
+    for mask in masks:
+        mask = mask.resize(shape)
+        bbox = mask.getbbox()
+        if bbox is not None:
+            bboxes.append(list(bbox))
+    return bboxes

adetailer/mediapipe.py

@@ -1,19 +1,33 @@
 from __future__ import annotations
 
+from functools import partial
+
 import numpy as np
-from PIL import Image
+from PIL import Image, ImageDraw
 
 from adetailer import PredictOutput
-from adetailer.common import create_mask_from_bbox
+from adetailer.common import create_bbox_from_mask, create_mask_from_bbox
 
 
 def mediapipe_predict(
-    model_type: int | str, image: Image.Image, confidence: float = 0.3
+    model_type: str, image: Image.Image, confidence: float = 0.3
+) -> PredictOutput:
+    mapping = {
+        "mediapipe_face_short": partial(mediapipe_face_detection, model_type=0),
+        "mediapipe_face_full": partial(mediapipe_face_detection, model_type=1),
+        "mediapipe_face_mesh": mediapipe_face_mesh,
+    }
+    if model_type in mapping:
+        func = mapping[model_type]
+        return func(image, confidence)
+    raise RuntimeError(f"[-] ADetailer: Invalid mediapipe model type: {model_type}")
+
+
+def mediapipe_face_detection(
+    model_type: int, image: Image.Image, confidence: float = 0.3
 ) -> PredictOutput:
     import mediapipe as mp
 
-    if isinstance(model_type, str):
-        model_type = mediapipe_model_name_to_type(model_type)
     img_width, img_height = image.size
 
     mp_face_detection = mp.solutions.face_detection
@@ -51,12 +65,47 @@ def mediapipe_predict(
     return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)
 
 
-def mediapipe_model_name_to_type(name: str) -> int:
-    name = name.lower()
-    mapping = {
-        "mediapipe_face_short": 0,
-        "mediapipe_face_full": 1,
-    }
-    if name not in mapping:
-        raise ValueError(f"[-] ADetailer: Invalid model name: {name}")
-    return mapping[name]
+def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput:
+    import mediapipe as mp
+    from scipy.spatial import ConvexHull
+
+    mp_face_mesh = mp.solutions.face_mesh
+    draw_util = mp.solutions.drawing_utils
+    drawing_styles = mp.solutions.drawing_styles
+
+    w, h = image.size
+
+    with mp_face_mesh.FaceMesh(
+        static_image_mode=True, max_num_faces=20, min_detection_confidence=confidence
+    ) as face_mesh:
+        arr = np.array(image)
+        pred = face_mesh.process(arr)
+
+        if pred.multi_face_landmarks is None:
+            return PredictOutput()
+
+        preview = arr.copy()
+        masks = []
+
+        for landmarks in pred.multi_face_landmarks:
+            draw_util.draw_landmarks(
+                image=preview,
+                landmark_list=landmarks,
+                connections=mp_face_mesh.FACEMESH_TESSELATION,
+                landmark_drawing_spec=None,
+                connection_drawing_spec=drawing_styles.get_default_face_mesh_tesselation_style(),
+            )
+
+            points = np.array([(land.x * w, land.y * h) for land in landmarks.landmark])
+            hull = ConvexHull(points)
+            vertices = hull.vertices
+            outline = list(zip(points[vertices, 0], points[vertices, 1]))
+
+            mask = Image.new("L", image.size, "black")
+            draw = ImageDraw.Draw(mask)
+            draw.polygon(outline, fill="white")
+            masks.append(mask)
+
+        bboxes = create_bbox_from_mask(masks, image.size)
+        preview = Image.fromarray(preview)
+        return PredictOutput(bboxes=bboxes, masks=masks, preview=preview)