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Zero
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"""KITTI evaluation code."""
from __future__ import annotations
import numpy as np
from vis4d.common.typing import NDArrayFloat, NDArrayNumber
from ..common import DepthEvaluator
def apply_garg_crop(mask: NDArrayNumber) -> NDArrayNumber:
"""Apply Garg ECCV16 crop to the mask.
Args:
mask (np.array): Mask to be cropped, in shape (..., H, W).
Returns:
np.array: Cropped mask, in shape (..., H', W').
"""
# crop used by Garg ECCV16
h, w = mask.shape[-2:]
crop = np.array(
[0.40810811 * h, 0.99189189 * h, 0.03594771 * w, 0.96405229 * w]
).astype(np.int32)
mask[..., crop[0] : crop[1], crop[2] : crop[3]] = 1
return mask
def apply_eigen_crop(mask: NDArrayNumber) -> NDArrayNumber:
"""Apply Eigen NIPS14 crop to the mask.
Args:
mask (np.array): Mask to be cropped, in shape (N, H, W).
Returns:
np.array: Cropped mask, in shape (N, H', W').
"""
# https://github.com/mrharicot/monodepth/utils/evaluate_kitti.py
h, w = mask.shape[-2:]
crop = np.array(
[0.3324324 * h, 0.91351351 * h, 0.0359477 * w, 0.96405229 * w]
).astype(np.int32)
mask[..., crop[0] : crop[1], crop[2] : crop[3]] = 1
return mask
class KITTIDepthEvaluator(DepthEvaluator):
"""KITTI depth evaluation class."""
METRIC_DEPTH = "depth"
def __init__(
self,
min_depth: float = 0.01,
max_depth: float = 80.0,
eval_crop: str | None = None,
) -> None:
"""Initialize KITTI depth evaluator."""
super().__init__(min_depth, max_depth)
self.eval_crop = eval_crop
self.reset()
def __repr__(self) -> str:
"""Concise representation of the dataset evaluator."""
return "KITTI evaluation for depth"
def _get_eval_mask(self, valid_mask: NDArrayNumber) -> NDArrayNumber:
"""Do Grag or Eigen cropping for testing."""
eval_mask = np.zeros_like(valid_mask)
if self.eval_crop == "garg_crop":
eval_mask = apply_garg_crop(eval_mask)
elif self.eval_crop == "eigen_crop":
eval_mask = apply_eigen_crop(eval_mask)
else:
eval_mask = np.ones_like(valid_mask)
return np.logical_and(valid_mask, eval_mask)
def _apply_mask(
self, prediction: NDArrayFloat, target: NDArrayFloat
) -> tuple[NDArrayFloat, NDArrayFloat]:
"""Apply mask to prediction and target."""
valid_mask = (target > self.min_depth) & (target < self.max_depth)
eval_mask = self._get_eval_mask(valid_mask)
prediction = prediction[eval_mask]
target = target[eval_mask]
return prediction, target
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