| """SHIFT depth estimation evaluator.""" |
|
|
| from __future__ import annotations |
|
|
| from vis4d.common.typing import NDArrayNumber |
|
|
| from ..common import DepthEvaluator |
|
|
|
|
| def apply_crop(depth: NDArrayNumber) -> NDArrayNumber: |
| """Apply crop to depth map to match SHIFT evaluation.""" |
| return depth[..., 0:740, :] |
|
|
|
|
| class SHIFTDepthEvaluator(DepthEvaluator): |
| """SHIFT depth estimation evaluation class.""" |
|
|
| def __init__(self, use_eval_crop: bool = True) -> None: |
| """Initialize the evaluator. |
| |
| Args: |
| use_eval_crop (bool): Whether to use the evaluation crop. |
| Default: True. |
| """ |
| super().__init__(min_depth=0.01, max_depth=80.0) |
| self.use_eval_crop = use_eval_crop |
|
|
| def __repr__(self) -> str: |
| """Concise representation of the dataset evaluator.""" |
| return "SHIFT Depth Estimation Evaluator" |
|
|
| def process_batch( |
| self, prediction: NDArrayNumber, groundtruth: NDArrayNumber |
| ) -> None: |
| """Process sample and update confusion matrix. |
| |
| Args: |
| prediction: Predictions of shape (N, H, W). |
| groundtruth: Groundtruth of shape (N, H, W). |
| """ |
| if self.use_eval_crop: |
| prediction = apply_crop(prediction) |
| groundtruth = apply_crop(groundtruth) |
| print(prediction.shape, groundtruth.shape) |
| super().process_batch(prediction, groundtruth) |
|
|
