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Keep the original dtype of the data when stretching #150

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merged 7 commits into from
Nov 20, 2023
Merged

Keep the original dtype of the data when stretching #150

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d34149c
Keep dtype when scaling the data
pnuu Nov 15, 2023
f23bb0c
Make sure stretching keeps the original dtype
pnuu Nov 15, 2023
b1d8d6f
Use float32 data for scale/offset attribute tests
pnuu Nov 16, 2023
5238b57
Ensure min and max stretch have the same dtype as the data
pnuu Nov 16, 2023
4b86901
Handle scalar stretch values
pnuu Nov 16, 2023
aee94f8
Refactor crude_stretch()
pnuu Nov 16, 2023
afc693a
Handle integer data in crude_stretch, refactor tests
pnuu Nov 17, 2023
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29 changes: 22 additions & 7 deletions trollimage/tests/test_image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1456,7 +1456,7 @@ def test_gamma(self):

def test_crude_stretch(self):
"""Check crude stretching."""
arr = np.arange(75).reshape(5, 5, 3)
arr = np.arange(75, dtype=np.float32).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
Expand All @@ -1467,19 +1467,34 @@ def test_crude_stretch(self):
enhs = img.data.attrs['enhancement_history'][0]
scale_expected = np.array([0.01388889, 0.01388889, 0.01388889])
offset_expected = np.array([0., -0.01388889, -0.02777778])
assert img.data.dtype == np.float32
np.testing.assert_allclose(enhs['scale'].values, scale_expected)
np.testing.assert_allclose(enhs['offset'].values, offset_expected)
np.testing.assert_allclose(red, arr[:, :, 0] / 72.)
np.testing.assert_allclose(green, (arr[:, :, 1] - 1.) / (73. - 1.))
np.testing.assert_allclose(blue, (arr[:, :, 2] - 2.) / (74. - 2.))

expected_red = arr[:, :, 0] / 72.
np.testing.assert_allclose(red, expected_red.astype(np.float32), rtol=1e-6)
expected_green = (arr[:, :, 1] - 1.) / (73. - 1.)
np.testing.assert_allclose(green, expected_green.astype(np.float32), rtol=1e-6)
expected_blue = (arr[:, :, 2] - 2.) / (74. - 2.)
np.testing.assert_allclose(blue, expected_blue.astype(np.float32), rtol=1e-6)

def test_crude_stretch_with_limits(self):
arr = np.arange(75).reshape(5, 5, 3).astype(float)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.crude_stretch(0, 74)
assert img.data.dtype == float
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I'm not sure how important of a check this is unless it is switched to 32-bit float, but 🤷‍♂️ not a big deal

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This is there just to make sure everything is not changed to float32 ("keep the original dtype").

np.testing.assert_allclose(img.data.values, arr / 74.)

def test_crude_stretch_integer_data(self):
arr = np.arange(75, dtype=int).reshape(5, 5, 3)
data = xr.DataArray(arr.copy(), dims=['y', 'x', 'bands'],
coords={'bands': ['R', 'G', 'B']})
img = xrimage.XRImage(data)
img.crude_stretch(0, 74)
assert img.data.dtype == np.float32
np.testing.assert_allclose(img.data.values, arr.astype(np.float32) / 74., rtol=1e-6)

def test_invert(self):
"""Check inversion of the image."""
arr = np.arange(75).reshape(5, 5, 3) / 75.
Expand Down Expand Up @@ -2325,10 +2340,10 @@ class TestXRImageSaveScaleOffset:
def setup_method(self) -> None:
"""Set up the test case."""
from trollimage import xrimage
data = xr.DataArray(np.arange(25).reshape(5, 5, 1), dims=[
data = xr.DataArray(np.arange(25, dtype=np.float32).reshape(5, 5, 1), dims=[
'y', 'x', 'bands'], coords={'bands': ['L']})
self.img = xrimage.XRImage(data)
rgb_data = xr.DataArray(np.arange(3 * 25).reshape(5, 5, 3), dims=[
rgb_data = xr.DataArray(np.arange(3 * 25, dtype=np.float32).reshape(5, 5, 3), dims=[
'y', 'x', 'bands'], coords={'bands': ['R', 'G', 'B']})
self.rgb_img = xrimage.XRImage(rgb_data)

Expand Down
52 changes: 34 additions & 18 deletions trollimage/xrimage.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1066,31 +1066,47 @@ def crude_stretch(self, min_stretch=None, max_stretch=None):
normalizes to the [0,1] range.
"""
if min_stretch is None:
non_band_dims = tuple(x for x in self.data.dims if x != 'bands')
min_stretch = self.data.min(dim=non_band_dims)
if max_stretch is None:
min_stretch = self._check_stretch_value(min_stretch, kind='min')
max_stretch = self._check_stretch_value(max_stretch, kind='max')
scale_factor = self._get_scale_factor(min_stretch, max_stretch)

attrs = self.data.attrs
offset = -min_stretch * scale_factor
self.data = np.multiply(self.data, scale_factor, dtype=scale_factor.dtype) + offset
self.data.attrs = attrs
self.data.attrs.setdefault('enhancement_history', []).append({'scale': scale_factor,
'offset': offset})

def _check_stretch_value(self, val, kind='min'):
if val is None:
non_band_dims = tuple(x for x in self.data.dims if x != 'bands')
max_stretch = self.data.max(dim=non_band_dims)
val = getattr(self.data, kind)(dim=non_band_dims)

if isinstance(min_stretch, (list, tuple)):
min_stretch = self.xrify_tuples(min_stretch)
if isinstance(max_stretch, (list, tuple)):
max_stretch = self.xrify_tuples(max_stretch)
if isinstance(val, (list, tuple)):
val = self.xrify_tuples(val)

try:
val = val.astype(self.data.dtype)
except AttributeError:
val = self.data.dtype.type(val)

return val

def _get_scale_factor(self, min_stretch, max_stretch):
delta = (max_stretch - min_stretch)
dtype = self._infer_scale_factor_dtype()
if isinstance(delta, xr.DataArray):
# fillna if delta is NaN
scale_factor = (1.0 / delta).fillna(0)
scale_factor = (1.0 / delta).fillna(0).astype(dtype)
else:
scale_factor = 1.0 / delta
attrs = self.data.attrs
offset = -min_stretch * scale_factor
self.data *= scale_factor
self.data += offset
self.data.attrs = attrs
self.data.attrs.setdefault('enhancement_history', []).append({'scale': scale_factor,
'offset': offset})
scale_factor = np.array(1.0 / delta, dtype=dtype)

return scale_factor

def _infer_scale_factor_dtype(self):
if np.issubdtype(self.data.dtype, np.integer):
return np.float32
return self.data.dtype

def stretch_hist_equalize(self, approximate=False):
"""Stretch the current image's colors through histogram equalization.
Expand Down
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