Use opencv when PIL is not available

Signed-off-by: guillemdb <[email protected]>

.github/workflows/push.yml

@@ -33,18 +33,12 @@ jobs:
       uses: actions/setup-python@v2
       with:
         python-version: "3.10"
-    - name: Setup Rye
-      id: setup-rye
-      uses: eifinger/setup-rye@v4
+    - name: Setup ruff
+      id: setup-ruff
+      uses: astral-sh/ruff-action@v3
       with:
-        enable-cache: true
-        cache-prefix: ubuntu-20.04-rye-check-${{ hashFiles('pyproject.toml') }}
-
-    - name: Run style check and linter
-      run: |
-        set -x
-        rye fmt --check
-        rye lint
+        version-file: "pyproject.toml"
+        args: "check --diff"
 
   pytest:
     name: Run Pytest
@@ -120,7 +114,7 @@ jobs:
         set -x
         # TODO: Figure out how to emulate a display in headless machines, and figure out why the commented files fail
         # SKIP_RENDER=True rye run pytest tests/test_registry.py tests/videogames/test_retro.py 
-        SKIP_RENDER=True rye run pytest tests/control tests/videogames/test_atari.py tests/videogames/test_nes.py tests/test_core.py
+        SKIP_RENDER=True rye run pytest tests/control tests/videogames/test_atari.py tests/videogames/test_nes.py tests/test_core.py tests/test_utils.py
 
     - name: Run code coverage on Ubuntu
       if: ${{ matrix.os == 'ubuntu-latest' }}

pyproject.toml

@@ -22,7 +22,7 @@ classifiers = [
     ]
 dependencies = [
     "numpy",
-    "pillow",
+    "pillow; sys_platform != 'darwin'",
     "fragile-gym",
     "opencv-python>=4.10.0.84",
     "pyglet==1.5.11",

src/plangym/utils.py

@@ -6,9 +6,17 @@
 from gymnasium.spaces import Box
 from gymnasium.wrappers.time_limit import TimeLimit
 import numpy
-from PIL import Image
 from pyvirtualdisplay import Display
 
+try:
+    from PIL import Image
+
+    USE_PIL = True
+except ImportError:  # pragma: no cover
+    import cv2
+
+    USE_PIL = False
+
 
 def get_display(visible=False, size=(400, 400), **kwargs):
     """Start a virtual display."""
@@ -59,7 +67,7 @@ def remove_time_limit(gym_env: gym.Env) -> gym.Env:
     return gym_env
 
 
-def process_frame(
+def process_frame_pil(
     frame: numpy.ndarray,
     width: int | None = None,
     height: int | None = None,
@@ -87,6 +95,62 @@ def process_frame(
     return numpy.array(frame)
 
 
+def process_frame_opencv(
+    frame: numpy.ndarray,
+    width: int | None = None,
+    height: int | None = None,
+    mode: str = "RGB",
+) -> numpy.ndarray:
+    """Resize an RGB frame to a specified shape and mode.
+
+    Use OpenCV to resize an RGB frame to a specified height and width \
+    or changing it to a different mode.
+
+    Args:
+        frame: Target numpy array representing the image that will be resized.
+        width: Width of the resized image.
+        height: Height of the resized image.
+        mode: Passed to cv2.cvtColor.
+
+    Returns:
+        The resized frame that matches the provided width and height.
+
+    """
+    height = height or frame.shape[0]
+    width = width or frame.shape[1]
+    frame = cv2.resize(frame, (width, height))
+    if mode == "GRAY":
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+    elif mode == "BGR":
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
+    return frame
+
+
+def process_frame(
+    frame: numpy.ndarray,
+    width: int | None = None,
+    height: int | None = None,
+    mode: str = "RGB",
+) -> numpy.ndarray:
+    """Resize an RGB frame to a specified shape and mode.
+
+    Use either PIL or OpenCV to resize an RGB frame to a specified height and width \
+    or changing it to a different mode.
+
+    Args:
+        frame: Target numpy array representing the image that will be resized.
+        width: Width of the resized image.
+        height: Height of the resized image.
+        mode: Passed to either Image.convert or cv2.cvtColor.
+
+    Returns:
+        The resized frame that matches the provided width and height.
+
+    """
+    func = process_frame_pil if USE_PIL else process_frame_opencv
+    return func(frame, width, height, mode)
+
+
 class GrayScaleObservation(gym.ObservationWrapper, gym.utils.RecordConstructorArgs):
     """Convert the image observation from RGB to gray scale.