vis4d/eval/metrics/depth.py

"""Depth estimation metrics."""

from __future__ import annotations

import numpy as np

from vis4d.common.typing import ArrayLike

from ..utils import check_shape_match, dense_inputs_to_numpy


def absolute_error(prediction: ArrayLike, target: ArrayLike) -> float:
    """Compute the absolute error.

    Args:
        prediction (NDArrayNumber): Prediction depth map, in shape (..., H, W).
        target (NDArrayNumber): Target depth map, in shape (..., H, W).

    Returns:
        float: Absolute error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    return np.mean(np.abs(prediction - target)).item()


def squared_relative_error(prediction: ArrayLike, target: ArrayLike) -> float:
    """Compute the squared relative error.

    Args:
        prediction (NDArrayNumber): Prediction depth map, in shape (..., H, W).
        target (NDArrayNumber): Target depth map, in shape (..., H, W).

    Returns:
        float: Square relative error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    return np.mean(np.square(prediction - target) / target).item()


def absolute_relative_error(prediction: ArrayLike, target: ArrayLike) -> float:
    """Compute the absolute relative error.

    Args:
        prediction (NDArrayNumber): Prediction depth map, in shape (..., H, W).
        target (NDArrayNumber): Target depth map, in shape (..., H, W).

    Returns:
        float: Absolute relative error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    return np.mean(np.abs(prediction - target) / target).item()


def root_mean_squared_error(prediction: ArrayLike, target: ArrayLike) -> float:
    """Compute the root mean squared error.

    Args:
        prediction (ArrayLike): Prediction depth map, in shape (..., H, W).
        target (ArrayLike): Target depth map, in shape (..., H, W).

    Returns:
        float: Root mean squared error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    squared_diff = np.square(prediction - target)
    return np.sqrt(np.mean(squared_diff)).item()


def root_mean_squared_error_log(
    prediction: ArrayLike, target: ArrayLike, epsilon: float = 1e-8
) -> float:
    """Compute the root mean squared error in log space.

    Args:
        prediction (ArrayLike): Prediction depth map, in shape (H, W).
        target (ArrayLike): Target depth map, in shape (H, W).
        epsilon (float, optional): Epsilon to avoid log(0). Defaults to 1e-6.

    Returns:
        float: Root mean squared error in log space.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    log_pred = np.log(prediction + epsilon)
    log_target = np.log(target + epsilon)
    squared_diff = np.square(log_pred - log_target)
    return np.sqrt(np.mean(squared_diff)).item()


def scale_invariant_log(
    prediction: ArrayLike, target: ArrayLike, epsilon: float = 1e-8
) -> float:
    """Compute the scale invariant log error.

    Args:
        prediction (ArrayLike): Prediction depth map, in shape (H, W).
        target (ArrayLike): Target depth map, in shape (H, W).
        epsilon (float, optional): Epsilon to avoid log(0). Defaults to 1e-6.

    Returns:
        float: Scale invariant log error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    log_diff = np.log(prediction + epsilon) - np.log(target + epsilon)
    return 100.0 * float(np.sqrt(np.var(log_diff)).mean())


def delta_p(
    prediction: ArrayLike, target: ArrayLike, power: float = 1
) -> float:
    """Compute the delta_p metric.

    Args:
        prediction (ArrayLike): Prediction depth map, in shape (H, W).
        target (ArrayLike): Target depth map, in shape (H, W).
        power (float, optional): Power of the threshold. Defaults to 1.

    Returns:
        float: Delta_p metric.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    return np.mean(
        np.maximum((target / prediction), (prediction / target)) < 1.25**power
    ).item()


def log_10_error(prediction: ArrayLike, target: ArrayLike) -> float:
    """Compute the log_10 error.

    Args:
        prediction (ArrayLike): Prediction depth map, in shape (H, W).
        target (ArrayLike): Target depth map, in shape (H, W).

    Returns:
        float: Log_10 error.
    """
    prediction, target = dense_inputs_to_numpy(prediction, target)
    check_shape_match(prediction, target)
    log10_diff = np.log10(prediction) - np.log10(target)
    return np.mean(np.abs(log10_diff)).item()