`ultralytics 8.2.80` add PIL type hints to `model()` (#15719)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.2.79"
+__version__ = "8.2.80"
 
 import os
 

ultralytics/engine/model.py

@@ -6,6 +6,7 @@ from typing import List, Union
 
 import numpy as np
 import torch
+from PIL import Image
 
 from ultralytics.cfg import TASK2DATA, get_cfg, get_save_dir
 from ultralytics.engine.results import Results
@@ -143,7 +144,7 @@ class Model(nn.Module):
 
     def __call__(
         self,
-        source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
+        source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         **kwargs,
     ) -> list:
@@ -504,7 +505,7 @@ class Model(nn.Module):
 
     def predict(
         self,
-        source: Union[str, Path, int, list, tuple, np.ndarray, torch.Tensor] = None,
+        source: Union[str, Path, int, Image.Image, list, tuple, np.ndarray, torch.Tensor] = None,
         stream: bool = False,
         predictor=None,
         **kwargs,
@@ -517,7 +518,7 @@ class Model(nn.Module):
         types of image sources and can operate in a streaming mode.
 
         Args:
-            source (str | Path | int | List[str] | List[Path] | List[int] | np.ndarray | torch.Tensor): The source
+            source (str | Path | int | PIL.Image | np.ndarray | torch.Tensor | List | Tuple): The source
                 of the image(s) to make predictions on. Accepts various types including file paths, URLs, PIL
                 images, numpy arrays, and torch tensors.
             stream (bool): If True, treats the input source as a continuous stream for predictions.