def _make_test_data(shape, input_dtype):
data = np.random.random_sample(shape)
if data.ndim == 3 and data.shape[-1] == 4:
# RGBA - make alpha fully opaque
data[..., -1] = 1.0
max_val = max_for_dtype(input_dtype)
if max_val != 1:
data *= max_val
data = data.astype(input_dtype)
return data
def _make_test_data(shape, input_dtype):
one_third = shape[0] // 3
two_third = 2 * one_third
data = np.zeros(shape, dtype=np.float64)
# 0.00 | 1.00 | 0.00
# 0.50 | 0.00 | 0.25
# 0.00 | 0.00 | 0.00
data[:, :one_third, one_third:two_third] = 1.0
data[:, one_third:two_third, :one_third] = 0.5
data[:, one_third:two_third, two_third:] = 0.25
max_val = max_for_dtype(input_dtype)
if max_val != 1:
data *= max_val
data = data.astype(input_dtype)
return data
def test_volume_clims_and_gamma(texture_format, input_dtype, clim_on_init):
"""Test volume visual with clims and gamma on shader.
Test is parameterized based on ``texture_format`` and should produce
relatively the same results for each format.
Currently just using np.ones since the angle of view made more complicated samples
challenging, but this confirms gamma and clims works in the shader.
The VolumeVisual defaults to the "grays" colormap so although we compare
data using RGBA arrays, each R/G/B channel should be the same.
"""
size = (40, 40)
if texture_format == '__dtype__':
texture_format = input_dtype
np.random.seed(0) # make tests the same every time
data = _make_test_data(size[:1] * 3, input_dtype)
clim = (0, 1)
new_clim = (0.3, 0.8)
max = max_for_dtype(input_dtype)
if max != 1:
clim = (clim[0] * max, clim[1] * max)
new_clim = (new_clim[0] * max, new_clim[1] * max)
kwargs = {}
if clim_on_init:
kwargs['clim'] = clim
with TestingCanvas(size=size, bgcolor="k") as c:
v = c.central_widget.add_view(border_width=0)
volume = scene.visuals.Volume(data,
interpolation='nearest',
parent=v.scene,
method='mip',
texture_format=texture_format,
**kwargs)
v.camera = 'arcball'
v.camera.fov = 0
v.camera.scale_factor = 40.0
v.camera.center = (19.5, 19.5, 19.5)
rendered = c.render()
_dtype = rendered.dtype
shape_ratio = rendered.shape[0] // data.shape[0]
rendered1 = downsample(rendered, shape_ratio,
axis=(0, 1)).astype(_dtype)
predicted = data.max(axis=1)
compare_render(predicted, rendered1)
# adjust contrast limits
volume.clim = new_clim
rendered2 = downsample(c.render(), shape_ratio,
axis=(0, 1)).astype(_dtype)
scaled_data = (np.clip(data, new_clim[0], new_clim[1]) -
new_clim[0]) / (new_clim[1] - new_clim[0])
predicted = scaled_data.max(axis=1)
compare_render(predicted, rendered2, previous_render=rendered)
# adjust gamma
volume.gamma = 2
rendered3 = downsample(c.render(), shape_ratio,
axis=(0, 1)).astype(_dtype)
predicted = (scaled_data**2).max(axis=1)
compare_render(predicted, rendered3, previous_render=rendered2)
def _get_orig_and_new_clims(input_dtype):
new_clim = (0.3, 0.8)
max_val = max_for_dtype(input_dtype)
if np.issubdtype(input_dtype, np.integer):
new_clim = (int(new_clim[0] * max_val), int(new_clim[1] * max_val))
return (0, max_val), new_clim
