refactor: Optimize distortion function for clarity and performance

- Added JAX Just-In-Time (JIT) compilation to the `distortion` function for improved performance.
- Reorganized variable unpacking and calculations for better readability and efficiency.
- Enhanced comments to clarify the steps involved in the distortion process, including normalization and radial/tangential distortion calculations.
- Updated the return values to directly reflect pixel coordinates after distortion.

app/camera/__init__.py

@@ -4,14 +4,15 @@ from datetime import datetime
 from typing import Any, TypeAlias, TypedDict, Optional
 
 from beartype import beartype
-from jax import Array
+import jax
 from jax import numpy as jnp
-from jaxtyping import Num, jaxtyped
+from jaxtyping import Num, jaxtyped, Array
 from typing_extensions import NotRequired
 
 CameraID: TypeAlias = str  # pylint: disable=invalid-name
 
 
+@jax.jit
 @jaxtyped(typechecker=beartype)
 def distortion(
     points_2d: Num[Array, "N 2"],
@@ -37,31 +38,36 @@ def distortion(
     Implementation based on OpenCV's distortion model:
     https://docs.opencv.org/4.10.0/d9/d0c/group__calib3d.html#ga69f2545a8b62a6b0fc2ee060dc30559d
     """
+    # unpack
+    fx, fy = K[0, 0], K[1, 1]
+    cx, cy = K[0, 2], K[1, 2]
     k1, k2, p1, p2, k3 = dist_coeffs
 
-    # Get principal point and focal length
-    cx, cy = K[0, 2], K[1, 2]
-    fx, fy = K[0, 0], K[1, 1]
-
-    # Convert to normalized coordinates
+    # normalize
     x = (points_2d[:, 0] - cx) / fx
     y = (points_2d[:, 1] - cy) / fy
+
+    # precompute r^2, r^4, r^6
     r2 = x * x + y * y
+    r4 = r2 * r2
+    r6 = r4 * r2
 
-    # Radial distortion
-    x_distort = x * (1 + k1 * r2 + k2 * r2 * r2 + k3 * r2 * r2 * r2)
-    y_distort = y * (1 + k1 * r2 + k2 * r2 * r2 + k3 * r2 * r2 * r2)
+    # radial term
+    radial = 1 + k1 * r2 + k2 * r4 + k3 * r6
 
-    # Tangential distortion
-    x_distort = x_distort + 2 * p1 * x * y + p2 * (r2 + 2 * x * x)
-    y_distort = y_distort + p1 * (r2 + 2 * y * y) + 2 * p2 * x * y
+    # tangential term
+    x_tan = 2 * p1 * x * y + p2 * (r2 + 2 * x * x)
+    y_tan = p1 * (r2 + 2 * y * y) + 2 * p2 * x * y
 
-    # Back to absolute coordinates
-    x_distort = x_distort * fx + cx
-    y_distort = y_distort * fy + cy
+    # apply both
+    x_dist = x * radial + x_tan
+    y_dist = y * radial + y_tan
 
-    # Combine distorted coordinates
-    return jnp.stack([x_distort, y_distort], axis=1)
+    # back to pixels
+    u = x_dist * fx + cx
+    v = y_dist * fy + cy
+
+    return jnp.stack([u, v], axis=1)
 
 
 @jaxtyped(typechecker=beartype)