fix: linting and enum for selecting openCV resampling algos

Author: anna-singleton-resolver

src/resolver_athena_client/client/athena_options.py

@@ -2,12 +2,13 @@
 
 from dataclasses import dataclass
 
-from PIL.Image import Resampling
-
 from resolver_athena_client.client.correlation import (
     CorrelationProvider,
     HashCorrelationProvider,
 )
+from resolver_athena_client.client.transformers.core import (
+    OpenCVResamplingAlgorithm,
+)
 
 
 @dataclass
@@ -69,4 +70,6 @@ class AthenaOptions:
     timeout: float | None = 120.0
     keepalive_interval: float | None = None
     compression_quality: int = 11  # Brotli quality level (0-11)
-    resampling_algorithm: Resampling = Resampling.LANCZOS
+    resampling_algorithm: OpenCVResamplingAlgorithm = (
+        OpenCVResamplingAlgorithm.BILINEAR
+    )

src/resolver_athena_client/client/transformers/core.py

@@ -6,30 +6,44 @@
 """
 
 import asyncio
-from io import BytesIO
+import enum
 
 import brotli
-from PIL import Image
+import cv2
+import numpy as np
 
 from resolver_athena_client.client.consts import EXPECTED_HEIGHT, EXPECTED_WIDTH
 from resolver_athena_client.client.models import ImageData
 from resolver_athena_client.generated.athena.models_pb2 import ImageFormat
-import cv2
-import numpy as np
 
 # Global optimization constants
 _target_size = (EXPECTED_WIDTH, EXPECTED_HEIGHT)
 _expected_raw_size = EXPECTED_WIDTH * EXPECTED_HEIGHT * 3
 
 
+class OpenCVResamplingAlgorithm(enum.Enum):
+    """Open CV Resampling Configuration.
+
+    Enum for ease of configuration and type-safety when selecting OpenCV
+    resampling algorithms.
+    """
+
+    NEAREST = cv2.INTER_NEAREST
+    BOX = cv2.INTER_AREA  # Best match for PIL's BOX
+    BILINEAR = cv2.INTER_LINEAR
+    LANCZOS = cv2.INTER_LANCZOS4
+
+
 def _is_raw_bgr_expected_size(data: bytes) -> bool:
     """Detect if data is already a raw BGR array of expected size."""
     return len(data) == _expected_raw_size
 
 
 async def resize_image(
     image_data: ImageData,
-    sampling_algorithm: Image.Resampling = Image.Resampling.LANCZOS,
+    sampling_algorithm: OpenCVResamplingAlgorithm = (
+        OpenCVResamplingAlgorithm.BILINEAR
+    ),
 ) -> ImageData:
     """Resize an image to expected dimensions.
 
@@ -62,7 +76,7 @@ def process_image() -> tuple[bytes, bool]:
             resized_img = img
         else:
             resized_img = cv2.resize(
-                img, _target_size, interpolation=cv2.INTER_LINEAR
+                img, _target_size, interpolation=sampling_algorithm.value
             )
 
         # openCV loads in BGR format by default, so we can directly convert to