feat(ml): composable ml (#9973)

* modularize model classes

* various fixes

* expose port

* change response

* round coordinates

* simplify preload

* update server

* simplify interface

simplify

* update tests

* composable endpoint

* cleanup

fixes

remove unnecessary interface

support text input, cleanup

* ew camelcase

* update server

server fixes

fix typing

* ml fixes

update locustfile

fixes

* cleaner response

* better repo response

* update tests

formatting and typing

rename

* undo compose change

* linting

fix type

actually fix typing

* stricter typing

fix detection-only response

no need for defaultdict

* update spec file

update api

linting

* update e2e

* unnecessary dimension

* remove commented code

* remove duplicate code

* remove unused imports

* add batch dim

machine-learning/app/models/transforms.py

@@ -1,3 +1,7 @@
+from io import BytesIO
+from typing import IO
+
+import cv2
 import numpy as np
 from numpy.typing import NDArray
 from PIL import Image
@@ -5,7 +9,7 @@ from PIL import Image
 _PIL_RESAMPLING_METHODS = {resampling.name.lower(): resampling for resampling in Image.Resampling}
 
 
-def resize(img: Image.Image, size: int) -> Image.Image:
+def resize_pil(img: Image.Image, size: int) -> Image.Image:
     if img.width < img.height:
         return img.resize((size, int((img.height / img.width) * size)), resample=Image.Resampling.BICUBIC)
     else:
@@ -13,7 +17,7 @@ def resize(img: Image.Image, size: int) -> Image.Image:
 
 
 # https://stackoverflow.com/a/60883103
-def crop(img: Image.Image, size: int) -> Image.Image:
+def crop_pil(img: Image.Image, size: int) -> Image.Image:
     left = int((img.size[0] / 2) - (size / 2))
     upper = int((img.size[1] / 2) - (size / 2))
     right = left + size
@@ -23,14 +27,36 @@ def crop(img: Image.Image, size: int) -> Image.Image:
 
 
 def to_numpy(img: Image.Image) -> NDArray[np.float32]:
-    return np.asarray(img.convert("RGB")).astype(np.float32) / 255.0
+    return np.asarray(img if img.mode == "RGB" else img.convert("RGB"), dtype=np.float32) / 255.0
 
 
 def normalize(
     img: NDArray[np.float32], mean: float | NDArray[np.float32], std: float | NDArray[np.float32]
 ) -> NDArray[np.float32]:
-    return (img - mean) / std
+    return np.divide(img - mean, std, dtype=np.float32)
 
 
 def get_pil_resampling(resample: str) -> Image.Resampling:
     return _PIL_RESAMPLING_METHODS[resample.lower()]
+
+
+def pil_to_cv2(image: Image.Image) -> NDArray[np.uint8]:
+    return cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)  # type: ignore
+
+
+def decode_pil(image_bytes: bytes | IO[bytes] | Image.Image) -> Image.Image:
+    if isinstance(image_bytes, Image.Image):
+        return image_bytes
+    image = Image.open(BytesIO(image_bytes) if isinstance(image_bytes, bytes) else image_bytes)
+    image.load()  # type: ignore
+    if not image.mode == "RGB":
+        image = image.convert("RGB")
+    return image
+
+
+def decode_cv2(image_bytes: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]:
+    if isinstance(image_bytes, bytes):
+        image_bytes = decode_pil(image_bytes)  # pillow is much faster than cv2
+    if isinstance(image_bytes, Image.Image):
+        return pil_to_cv2(image_bytes)
+    return image_bytes