vis4d.eval.shift.depth

SHIFT depth estimation evaluator.

Functions

apply_crop(depth)

Apply crop to depth map to match SHIFT evaluation.

Classes

SHIFTDepthEvaluator([use_eval_crop])

SHIFT depth estimation evaluation class.
apply_crop(depth)[source]

Apply crop to depth map to match SHIFT evaluation.

Return type:

Union[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[int32]], ndarray[Any, dtype[int64]], ndarray[Any, dtype[uint8]], ndarray[Any, dtype[uint16]], ndarray[Any, dtype[uint32]]]

class SHIFTDepthEvaluator(use_eval_crop=True)[source]

SHIFT depth estimation evaluation class.

__init__(use_eval_crop=True)[source]

Initialize the evaluator.

Parameters:

use_eval_crop (bool) – Whether to use the evaluation crop. Default: True.

__repr__()[source]

Concise representation of the dataset evaluator.

Return type:

str

process_batch(prediction, groundtruth)[source]

Process sample and update confusion matrix.

Parameters:

  • prediction (Union[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[int32]], ndarray[Any, dtype[int64]], ndarray[Any, dtype[uint8]], ndarray[Any, dtype[uint16]], ndarray[Any, dtype[uint32]]]) – Predictions of shape (N, H, W).

  • groundtruth (Union[ndarray[Any, dtype[bool_]], ndarray[Any, dtype[float32]], ndarray[Any, dtype[float64]], ndarray[Any, dtype[int32]], ndarray[Any, dtype[int64]], ndarray[Any, dtype[uint8]], ndarray[Any, dtype[uint16]], ndarray[Any, dtype[uint32]]]) – Groundtruth of shape (N, H, W).

Return type:

None