type hints for benchmark (and also some missing ones in dlclive)

dlclive/benchmark.py

@@ -12,7 +12,7 @@
 import sys
 import warnings
 import subprocess
-import typing
+from typing import List, Optional, Tuple, Union
 import pickle
 import colorcet as cc
 from PIL import ImageColor
@@ -148,22 +148,22 @@ def get_system_info() -> dict:
 
 
 def benchmark(
-    model_path,
-    video_path,
-    tf_config=None,
-    resize=None,
-    pixels=None,
-    cropping=None,
-    dynamic=(False, 0.5, 10),
-    n_frames=1000,
-    print_rate=False,
-    display=False,
-    pcutoff=0.0,
-    display_radius=3,
-    cmap="bmy",
-    save_poses=False,
-    save_video=False,
-    output=None,
+    model_path: str,
+    video_path: str,
+    tf_config: Optional[tf.ConfigProto] = None,
+    resize: Optional[float] = None,
+    pixels: Optional[int] = None,
+    cropping: Optional[List[int]] = None,
+    dynamic: Tuple[bool, float, int] = (False, 0.5, 10),
+    n_frames: int = 1000,
+    print_rate: bool = False,
+    display: bool = False,
+    pcutoff: float = 0.0,
+    display_radius: int = 3,
+    cmap: str = "bmy",
+    save_poses: bool = False,
+    save_video: bool = False,
+    output: Optional[str] = None,
 ) -> typing.Tuple[np.ndarray, tuple, bool, dict]:
     """ Analyze DeepLabCut-live exported model on a video:
     Calculate inference time,
@@ -516,22 +516,22 @@ def save_inf_times(
 
 
 def benchmark_videos(
-    model_path,
-    video_path,
-    output=None,
-    n_frames=1000,
-    tf_config=None,
-    resize=None,
-    pixels=None,
-    cropping=None,
-    dynamic=(False, 0.5, 10),
-    print_rate=False,
-    display=False,
-    pcutoff=0.5,
-    display_radius=3,
-    cmap="bmy",
-    save_poses=False,
-    save_video=False,
+    model_path: str,
+    video_path: Union[str, List[str]],
+    output: Optional[str] = None,
+    n_frames: int = 1000,
+    tf_config: Optional[tf.ConfigProto] = None,
+    resize: Optional[Union[float, List[float]]] = None,
+    pixels: Optional[Union[int, List[int]]] = None,
+    cropping: Optional[List[int]] = None,
+    dynamic: Tuple[bool, float, int] = (False, 0.5, 10),
+    print_rate: bool = False,
+    display: bool = False,
+    pcutoff: float = 0.5,
+    display_radius: int = 3,
+    cmap: str = "bmy",
+    save_poses: bool = False,
+    save_video: bool = False,
 ):
     """Analyze videos using DeepLabCut-live exported models.
     Analyze multiple videos and/or multiple options for the size of the video

dlclive/dlclive.py

@@ -181,7 +181,7 @@ def parameterization(self) -> dict:
         """
         return {param: getattr(self, param) for param in self.PARAMETERS}
 
-    def process_frame(self, frame):
+    def process_frame(self, frame: np.ndarray) -> np.ndarray:
         """
         Crops an image according to the object's cropping and dynamic properties.
 
@@ -237,7 +237,7 @@ def process_frame(self, frame):
 
         return frame
 
-    def init_inference(self, frame=None, **kwargs):
+    def init_inference(self, frame=None, **kwargs) -> np.ndarray:
         """
         Load model and perform inference on first frame -- the first inference is usually very slow.
 
@@ -376,7 +376,7 @@ def init_inference(self, frame=None, **kwargs):
 
         return pose
 
-    def get_pose(self, frame=None, **kwargs):
+    def get_pose(self, frame=None, **kwargs) -> np.ndarray:
         """
         Get the pose of an image