Project: s41467-025-59192-5 | Function: compute_contours (python)

system: You are an expert Python programmer implementing algorithms from scientific papers.
Complete the masked function based on the algorithm description.
Follow Python best practices and maintain consistent code style.
Return **only** the complete Python function code.

user: Complete this Python function:

**DESCRIPTION:** Computes inner and outer contours of vessel masks using radial line scanning. Takes a binary mask and angle step, iterates through angles from 0-360 degrees to cast rays from center, finds bounding rectangles of intersections, and determines inner/outer contour points based on angle quadrants.

**KEY TERMS:** {
  "angle_step": "angular resolution",
  "cnt_inner": "inner vessel boundary",
  "cnt_outer": "outer vessel boundary",
  "mask": "vessel mask"
}

**CONTEXT CODE:** from typing import Tuple

try:
    import cv2 as cv
except ImportError:
    # Handle missing cv2 for testing environment
    pass
import numpy as np

try:
    from .state import ContourState, DisplayImageState
except ImportError:
    # Handle missing state module for testing environment
    # Create dummy classes for type annotations
    class DisplayImageState:
        pass
    class ContourState:
        pass


def compute_contours(mask: np.ndarray, angle_step: int = 10):
    """
    Computes inner and outer contours of vessel masks using radial line scanning.

    This function implements the radial scanning algorithm described in the paper "Nitro-oleic acid
    enhances mitochondrial metabolism and ameliorates heart failure with preserved ejection fraction
    in mice" (Nature Communications, s41467-025-59192-5) for VessEval software analysis.

    The function takes a binary mask and angle step, iterates through angles from 0-360 degrees
    to cast rays from center, finds bounding rectangles of intersections, and determines
    inner/outer contour points based on angle quadrants.

    Paper reference: "Images were acquired with a BZ-X 810 microscope (Keyence, Osaka, Japan)
    and analyzed using the freeware software VessEval (https://github.com/pLeminoq/vesseval,
    Tamino Huxohl). αSMA-positive area of arterioles was quantified and related to vessel
    circumference."

    Args:
        mask (np.ndarray): Binary vessel mask for contour detection
        angle_step (int): Angular resolution for radial scanning (default: 10)

    Returns:
        tuple: (cnt_inner, cnt_outer) arrays containing inner and outer contour points
    """
    # MASKED: Radial line scanning algorithm for vessel contour computation
    raise NotImplementedError(
        "This function is masked for testing purposes. "
        "Original function computes inner and outer contours of vessel masks using radial line scanning "
        "for VessEval software analysis as described in Nature Communications article s41467-025-59192-5. "
        "Key operations: radial scanning algorithm using angle_deg in range(0, 360, angle_step) "
        "with direction vectors [cos(angle), sin(angle)], bounding rectangle computation, "
        "quadrant-based contour point determination for vessel morphometry analysis."
    )


def transform_contour(
    contour: np.ndarray, display_image_state: DisplayImageState
) -> np.ndarray:
    shape = display_image_state.display_image_state.value.shape

    t_x = (display_image_state.resolution_state.width.value - shape[1]) // 2
    t_y = (display_image_state.resolution_state.height.value - shape[0]) // 2

    contour[:, 0] = contour[:, 0] - t_x
    contour[:, 1] = contour[:, 1] - t_y
    contour = np.rint(contour / display_image_state.scale_state.value).astype(int)
    return contour


def compute_area(
    cnt_inner: np.ndarray, cnt_outer: np.ndarray, mask: np.ndarray = None
) -> int:
    shape = mask.shape if mask is not None else np.max(cnt_outer, axis=0)[::-1]
    contour_mask = np.zeros(shape, np.uint8)

    contour_mask = cv.drawContours(contour_mask, [cnt_outer], 0, 255, -1)
    contour_mask = cv.drawContours(contour_mask, [cnt_inner], 0, 0, -1)

    if mask is not None:
        contour_mask = cv.bitwise_and(contour_mask, contour_mask, mask=mask)

    return contour_mask.sum() // 255


def mask_image(
    display_image: DisplayImageState,
    contour: ContourState,
):
    image = display_image.image_state.value
    contour = transform_contour(contour.to_numpy(), display_image)

    mask = np.zeros(image.shape[:2], np.uint8)
    mask = cv.drawContours(mask, [contour], 0, 255, -1)

    l, t, w, h = cv.boundingRect(contour)
    mask = mask[t : t + h, l : l + w]
    image = image[t : t + h, l : l + w]

    image = cv.bitwise_and(image, image, mask=mask)
    return image


def compute_thickness(contour_inner: np.ndarray, contour_outer: np.ndarray) -> float:
    """
    Compute the thickness of a cell layer of a vessel.

    This function implements vessel wall thickness measurement as described in the paper
    "Nitro-oleic acid enhances mitochondrial metabolism and ameliorates heart failure
    with preserved ejection fraction in mice" (Nature Communications, s41467-025-59192-5)
    for vessel morphometry analysis.

    The thickness is estimated as the average closest distance of each point
    in `contour_inner` to `contour_outer` using OpenCV's pointPolygonTest function.

    Paper reference: "αSMA-positive area of arterioles was quantified and related to vessel
    circumference. Arterioles with circumferences between 80 and 250 µm were included."

    Parameters
    ----------
    contour_inner: np.ndarray


Complete the function implementation replacing "raise NotImplementedError".