fix: mediapipe, ultralytics import

adetailer/__version__.py

@@ -1 +1 @@
-__version__ = "23.7.9"
+__version__ = "23.7.10.dev0"

adetailer/mediapipe.py

@@ -2,6 +2,7 @@ from __future__ import annotations
 
 from functools import partial
 
+import mediapipe as mp
 import numpy as np
 from PIL import Image, ImageDraw
 
@@ -28,8 +29,6 @@ def mediapipe_predict(
 def mediapipe_face_detection(
     model_type: int, image: Image.Image, confidence: float = 0.3
 ) -> PredictOutput:
-    import mediapipe as mp
-
     img_width, img_height = image.size
 
     mp_face_detection = mp.solutions.face_detection
@@ -85,8 +84,6 @@ def get_convexhull(points: np.ndarray) -> list[tuple[int, int]]:
 
 
 def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictOutput:
-    import mediapipe as mp
-
     mp_face_mesh = mp.solutions.face_mesh
     draw_util = mp.solutions.drawing_utils
     drawing_styles = mp.solutions.drawing_styles
@@ -130,8 +127,6 @@ def mediapipe_face_mesh(image: Image.Image, confidence: float = 0.3) -> PredictO
 def mediapipe_face_mesh_eyes_only(
     image: Image.Image, confidence: float = 0.3
 ) -> PredictOutput:
-    import mediapipe as mp
-
     mp_face_mesh = mp.solutions.face_mesh
 
     left_idx = np.array(list(mp_face_mesh.FACEMESH_LEFT_EYE)).flatten()

adetailer/ultralytics.py

@@ -4,14 +4,14 @@ from pathlib import Path
 
 import cv2
 from PIL import Image
+from torchvision.transforms.functional import to_pil_image
+from ultralytics import YOLO
 
 from adetailer import PredictOutput
 from adetailer.common import create_mask_from_bbox
 
 
 def load_yolo(model_path: str | Path):
-    from ultralytics import YOLO
-
     try:
         return YOLO(model_path)
     except ModuleNotFoundError:
@@ -57,7 +57,5 @@ def mask_to_pil(masks, shape: tuple[int, int]) -> list[Image.Image]:
     shape: tuple[int, int]
         (width, height) of the original image
     """
-    from torchvision.transforms.functional import to_pil_image
-
     n = masks.shape[0]
     return [to_pil_image(masks[i], mode="L").resize(shape) for i in range(n)]