fix CI by upgrading mypy and downgrading numpy

requirements.dev.txt

@@ -1,5 +1,5 @@
 flake8==5.0.4
-mypy==0.971
+mypy==0.991
 pylint==2.14.5
 pytest==7.1.2
 pytest-watch==4.2.0

requirements.txt

@@ -1,3 +1,4 @@
+numpy<1.24
 opencv-python==4.6.0.66
 Pillow==9.2.0
 pyfakewebcam==0.1.0

tf_bodypix/cli.py

@@ -655,7 +655,7 @@ def get_app(self, args: argparse.Namespace) -> AbstractWebcamFilterApp:
 }
 
 
-def parse_args(argv: List[str] = None) -> argparse.Namespace:
+def parse_args(argv: Optional[List[str]] = None) -> argparse.Namespace:
     parser = argparse.ArgumentParser(
         'TensorFlow BodyPix (TF BodyPix)',
         formatter_class=argparse.ArgumentDefaultsHelpFormatter
@@ -678,7 +678,7 @@ def run(args: argparse.Namespace):
     sub_command.run(args)
 
 
-def main(argv: List[str] = None):
+def main(argv: Optional[List[str]] = None):
     args = parse_args(argv)
     if args.debug:
         logging.getLogger().setLevel(logging.DEBUG)

tf_bodypix/model.py

@@ -198,7 +198,7 @@ def is_all_part_names(part_names: Optional[List[str]]) -> bool:
 
 def get_filtered_part_segmentation(
     part_segmentation: np.ndarray,
-    part_names: List[str] = None
+    part_names: Optional[List[str]] = None
 ):
     if is_all_part_names(part_names):
         return part_segmentation
@@ -269,8 +269,8 @@ def get_scaled_part_heatmap_scores(self, **kwargs) -> np.ndarray:
 
     def get_scaled_part_segmentation(
         self,
-        mask: np.ndarray = None,
-        part_names: List[str] = None,
+        mask: Optional[np.ndarray] = None,
+        part_names: Optional[List[str]] = None,
         outside_mask_value: int = -1,
         resize_method: Optional[str] = None
     ) -> np.ndarray:
@@ -312,7 +312,7 @@ def get_mask(
     def get_part_mask(
         self,
         mask: np.ndarray,
-        part_names: List[str] = None,
+        part_names: Optional[List[str]] = None,
         resize_method: Optional[str] = None
     ) -> np.ndarray:
         if is_all_part_names(part_names):
@@ -331,8 +331,8 @@ def get_part_mask(
     def get_colored_part_mask(
         self,
         mask: np.ndarray,
-        part_colors: List[T_Color] = None,
-        part_names: List[str] = None,
+        part_colors: Optional[List[T_Color]] = None,
+        part_names: Optional[List[str]] = None,
         resize_method: Optional[str] = None
     ) -> np.ndarray:
         part_segmentation = self.get_scaled_part_segmentation(
@@ -597,8 +597,8 @@ def get_architecture_from_model_path(model_path: str) -> str:
 
 def load_model(
     model_path: str,
-    output_stride: int = None,
-    architecture_name: str = None,
+    output_stride: Optional[int] = None,
+    architecture_name: Optional[str] = None,
     **kwargs
 ):
     if not output_stride:

tf_bodypix/source.py

@@ -45,7 +45,7 @@ def get_webcam_image_source(webcam_number: int, **kwargs) -> T_ImageSource:
 @contextmanager
 def get_simple_image_source(
     path: str,
-    image_size: ImageSize = None,
+    image_size: Optional[ImageSize] = None,
     **_
 ) -> Iterator[Iterable[ImageArray]]:
     local_image_path = get_file(path)

tf_bodypix/utils/dist.py

@@ -1,9 +1,11 @@
-def get_requirement_groups(requirement):
+def get_requirement_groups(requirement):  # pylint: disable=too-many-return-statements
     requirement_lower = requirement.lower()
     if 'tensorflow' in requirement_lower:
         return ['tf']
     if 'tfjs' in requirement_lower:
         return ['tfjs']
+    if 'numpy' in requirement_lower:
+        return ['tfjs']
     if 'pillow' in requirement_lower:
         return ['image']
     if 'opencv' in requirement_lower:

tf_bodypix/utils/image.py

@@ -229,7 +229,7 @@ def rgb_to_bgr(image: np.ndarray) -> np.ndarray:
 
 def _load_image_using_tf(
     local_image_path: str,
-    image_size: ImageSize = None
+    image_size: Optional[ImageSize] = None
 ) -> np.ndarray:
     image = tf.keras.preprocessing.image.load_img(
         local_image_path
@@ -242,7 +242,7 @@ def _load_image_using_tf(
 
 def _load_image_using_pillow(
     local_image_path: str,
-    image_size: ImageSize = None
+    image_size: Optional[ImageSize] = None
 ) -> np.ndarray:
     with PIL.Image.open(local_image_path) as image:
         image_array = np.asarray(image)
@@ -253,7 +253,7 @@ def _load_image_using_pillow(
 
 def load_image(
     local_image_path: str,
-    image_size: ImageSize = None
+    image_size: Optional[ImageSize] = None
 ) -> np.ndarray:
     if tf is not None:
         return _load_image_using_tf(local_image_path, image_size=image_size)

tf_bodypix/utils/opencv.py

@@ -2,7 +2,7 @@
 from collections import deque
 from contextlib import contextmanager
 from time import monotonic, sleep
-from typing import Callable, ContextManager, Deque, Iterable, Iterator, Union
+from typing import Callable, ContextManager, Deque, Iterable, Iterator, Optional, Union
 
 import cv2
 import numpy as np
@@ -23,7 +23,7 @@
 def iter_read_raw_video_images(
     video_capture: cv2.VideoCapture,
     repeat: bool = False,
-    is_stopped: Callable[[], bool] = None
+    is_stopped: Optional[Callable[[], bool]] = None
 ) -> Iterable[ImageArray]:
     while is_stopped is None or not is_stopped():
         grabbed, image_array = video_capture.read()
@@ -41,7 +41,7 @@ def iter_read_raw_video_images(
 
 def iter_resize_video_images(
     video_images: Iterable[ImageArray],
-    image_size: ImageSize = None,
+    image_size: Optional[ImageSize] = None,
     interpolation: int = cv2.INTER_LINEAR
 ) -> Iterable[ImageArray]:
     is_first = True
@@ -70,7 +70,7 @@ def iter_convert_video_images_to_rgb(
 
 def iter_delay_video_images_to_fps(
     video_images: Iterable[ImageArray],
-    fps: float = None
+    fps: Optional[float] = None
 ) -> Iterable[np.ndarray]:
     if not fps or fps <= 0:
         LOGGER.info('no fps requested, providing images from source (without delay)')
@@ -120,10 +120,10 @@ def iter_delay_video_images_to_fps(
 
 def iter_read_video_images(
     video_capture: cv2.VideoCapture,
-    image_size: ImageSize = None,
+    image_size: Optional[ImageSize] = None,
     interpolation: int = cv2.INTER_LINEAR,
     repeat: bool = True,
-    fps: float = None
+    fps: Optional[float] = None
 ) -> Iterable[np.ndarray]:
     video_images: Iterable[np.ndarray]
     video_images = iter_read_raw_video_images(video_capture, repeat=repeat)
@@ -138,11 +138,11 @@ def iter_read_video_images(
 @contextmanager
 def get_video_image_source(  # pylint: disable=too-many-locals
     path: Union[str, int],
-    image_size: ImageSize = None,
+    image_size: Optional[ImageSize] = None,
     download: bool = True,
-    fps: float = None,
-    fourcc: str = None,
-    buffer_size: int = None,
+    fps: Optional[float] = None,
+    fourcc: Optional[str] = None,
+    buffer_size: Optional[int] = None,
     **_
 ) -> Iterator[Iterable[ImageArray]]:
     local_path: Union[str, int]
@@ -190,7 +190,7 @@ def get_video_image_source(  # pylint: disable=too-many-locals
 
 def get_webcam_image_source(
     path: Union[str, int],
-    fourcc: str = None,
+    fourcc: Optional[str] = None,
     buffer_size: int = 1,
     **kwargs
 ) -> ContextManager[Iterable[ImageArray]]:

tf_bodypix/utils/timer.py

@@ -27,7 +27,7 @@ def start(self):
         current_time = time()
         self.interval_start_time = current_time
 
-    def _set_current_step_name(self, step_name: str, current_time: float = None):
+    def _set_current_step_name(self, step_name: str, current_time: Optional[float] = None):
         if step_name == self.current_step_name:
             return
         if current_time is None:
@@ -43,7 +43,7 @@ def _set_current_step_name(self, step_name: str, current_time: float = None):
             self.current_step_name = step_name
         self.current_step_start_time = current_time
 
-    def on_frame_start(self, initial_step_name: str = None):
+    def on_frame_start(self, initial_step_name: Optional[str] = None):
         self.frame_start_time = time()
         self.current_step_name = initial_step_name
         self.current_step_start_time = self.frame_start_time