def test_image_clims_and_gamma(input_dtype, texture_format, num_channels,
clim_on_init, data_on_init):
"""Test image visual with clims and gamma on shader."""
size = (40, 40)
if texture_format == '__dtype__':
texture_format = input_dtype
shape = size + (num_channels, ) if num_channels > 0 else size
np.random.seed(0)
data = _make_test_data(shape, input_dtype)
orig_clim, new_clim = _get_orig_and_new_clims(input_dtype)
# 16-bit integers and above seem to have precision loss when scaled on the CPU
is_16int_cpu_scaled = (np.dtype(input_dtype).itemsize >= 2
and np.issubdtype(input_dtype, np.integer)
and texture_format is None)
clim_atol = 2 if is_16int_cpu_scaled else 1
gamma_atol = 3 if is_16int_cpu_scaled else 2
kwargs = {}
if clim_on_init:
kwargs['clim'] = orig_clim
if data_on_init:
kwargs['data'] = data
# default is RGBA, anything except auto requires reformat
set_data_fails = (num_channels != 4 and texture_format is not None
and texture_format != 'auto')
with TestingCanvas(size=size[::-1], bgcolor="w") as c:
image = Image(cmap='grays',
texture_format=texture_format,
parent=c.scene,
**kwargs)
if not data_on_init:
_set_image_data(image, data, set_data_fails)
if set_data_fails:
return
rendered = c.render()
_dtype = rendered.dtype
shape_ratio = rendered.shape[0] // data.shape[0]
rendered1 = downsample(rendered, shape_ratio,
axis=(0, 1)).astype(_dtype)
_compare_render(data, rendered1)
# adjust color limits
image.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])
_compare_render(scaled_data, rendered2, rendered1, atol=clim_atol)
# adjust gamma
image.gamma = 2
rendered3 = downsample(c.render(), shape_ratio,
axis=(0, 1)).astype(_dtype)
_compare_render(scaled_data**2, rendered3, rendered2, atol=gamma_atol)
def test_image(is_3d):
"""Test image visual"""
size = (100, 50)
with TestingCanvas(size=size, bgcolor='w') as c:
image = Image(cmap='grays', clim=[0, 1], parent=c.scene)
shape = (size[1]-10, size[0]-10) + ((3,) if is_3d else ())
np.random.seed(379823)
data = np.random.rand(*shape)
image.set_data(data)
assert_image_approved(c.render(), "visuals/image%s.png" %
("_rgb" if is_3d else "_mono"))
# change to auto clims after first draw
image.clim = "auto"
assert_image_approved(c.render(), "visuals/image%s.png" %
("_rgb" if is_3d else "_mono"))
def test_image_clims_and_gamma():
"""Test image visual with clims and gamma on shader."""
size = (40, 40)
with TestingCanvas(size=size, bgcolor="w") as c:
for three_d in (True,):
shape = size + ((3,) if three_d else ())
np.random.seed(0)
image = Image(cmap='grays', clim=[0, 1], parent=c.scene)
data = np.random.rand(*shape)
image.set_data(data)
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 = _make_rgba(data)
assert np.allclose(predicted, rendered1, atol=1)
# adjust contrast limits
new_clim = (0.3, 0.8)
image.clim = new_clim
rendered2 = downsample(c.render(), shape_ratio, axis=(0, 1)).astype(_dtype)
scaled_data = np.clip((data - new_clim[0]) / np.diff(new_clim)[0], 0, 1)
predicted = _make_rgba(scaled_data)
assert np.allclose(predicted, rendered2, atol=1)
assert not np.allclose(rendered1, rendered2, atol=10)
# adjust gamma
image.gamma = 2
rendered3 = downsample(c.render(), shape_ratio, axis=(0, 1)).astype(_dtype)
predicted = _make_rgba(scaled_data ** 2)
assert np.allclose(
predicted.astype(np.float), rendered3.astype(np.float), atol=2
)
assert not np.allclose(
rendered2.astype(np.float), rendered3.astype(np.float), atol=10
)