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# -*- coding: utf-8 -*-
# -----------------------------------------------------------------------------
# Copyright (c) Vispy Development Team. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
"""Test utilities for comparing rendered arrays with expected array results."""
from typing import Optional, Any
import numpy as np
import pytest
try:
from numpy.typing import ArrayLike, DtypeLike
except ImportError:
ArrayLike = np.ndarray
DtypeLike = Any
def compare_render(
orig_data: ArrayLike,
rendered_data: ArrayLike,
previous_render: Optional[ArrayLike] = None,
atol: Optional[float] = 1.0
):
"""Compare an expected original array with the rendered result.
Parameters
----------
orig_data
Expected output result array. This will be converted to an RGBA array
to be compared against the rendered data.
rendered_data
Actual rendered result as an RGBA 8-bit unsigned array.
previous_render
Previous instance of a render that the current render should not be
equal to.
atol
Absolute tolerance to be passed to
:func:`numpy.testing.assert_allclose`.
"""
predicted = make_rgba(orig_data)
np.testing.assert_allclose(rendered_data.astype(float), predicted.astype(float), atol=atol)
if previous_render is not None:
# assert not allclose
pytest.raises(AssertionError, np.testing.assert_allclose,
rendered_data, previous_render, atol=10)
def max_for_dtype(input_dtype: DtypeLike):
"""Get maximum value an image array should have for a specific dtype.
This is max int for each integer type or 1.0 for floating point types.
"""
if np.issubdtype(input_dtype, np.integer):
max_val = np.iinfo(input_dtype).max
else:
max_val = 1.0
return max_val
def make_rgba(data_in: ArrayLike) -> ArrayLike:
"""Convert any array to an RGBA array.
RGBA arrays have 3 dimensions where the last represents the channels. If
an Alpha channel needs to be added it will be made completely opaque.
Returns
-------
3D RGBA unsigned 8-bit array
"""
max_val = max_for_dtype(data_in.dtype)
if data_in.ndim == 3 and data_in.shape[-1] == 1:
data_in = data_in.squeeze()
if data_in.ndim == 2:
out = np.stack([data_in] * 4, axis=2)
out[:, :, 3] = max_val
elif data_in.shape[-1] == 3:
out = np.concatenate((data_in, np.ones((*data_in.shape[:2], 1)) * max_val), axis=2)
else:
out = data_in
return np.round((out.astype(np.float32) * 255 / max_val)).astype(np.uint8)
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