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Preserve pixel shape for single-band NDVI selections #187

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Preserve pixel shape for single-band NDVI selections #187

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df02aa8
Cover singleton and mixed NDVI band selectors
antonsoo Oct 18, 2026
ea82b6f
Reduce singleton selected-band axes when computing NDVI
antonsoo Oct 18, 2026
f879f2e
Cover loaded-image scalar band dimensions
antonsoo Oct 18, 2026
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4 changes: 2 additions & 2 deletions spectral/algorithms/algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1245,10 +1245,10 @@ def ndvi(data: np.ndarray | Image, red: int | slice | Sequence[int],
'''

r = data[:, :, red].astype(float)
if len(r.shape) == 3 and r.shape[2] > 1:
if len(r.shape) == 3:
r = np.mean(r, axis=2)
n = data[:, :, nir].astype(float)
if len(n.shape) == 3 and n.shape[2] > 1:
if len(n.shape) == 3:
n = np.mean(n, axis=2)

return (n - r) / (n + r)
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36 changes: 36 additions & 0 deletions spectral/tests/test_algorithms.py
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Original file line number Diff line number Diff line change
Expand Up @@ -313,6 +313,42 @@ def test_multi_band_range_is_unbiased_mean(self, wide_data):
n = np.mean(wide_data[:, :, nir].astype(float), axis=2)
assert_allclose(result, (n - r) / (n + r))

@pytest.mark.parametrize('red,nir,red_bands,nir_bands', [
(2, 6, [2], [6]),
([2], 6, [2], [6]),
(2, [6], [2], [6]),
([2], [6], [2], [6]),
(slice(2, 3), 6, [2], [6]),
(2, slice(6, 7), [2], [6]),
(np.array([2]), np.array([6]), [2], [6]),
((2,), slice(6, 7), [2], [6]),
(slice(2, 4), [6], [2, 3], [6]),
([2], slice(5, 8), [2], [5, 6, 7]),
])
def test_singleton_band_selectors_preserve_pixel_shape(
self, wide_data, red, nir, red_bands, nir_bands):
expected = np.empty(wide_data.shape[:2])
for row, col in np.ndindex(expected.shape):
r = sum(float(wide_data[row, col, b]) for b in red_bands) / len(red_bands)
n = sum(float(wide_data[row, col, b]) for b in nir_bands) / len(nir_bands)
expected[row, col] = (n - r) / (n + r)

result = ndvi(wide_data, red, nir)

assert result.shape == expected.shape
assert_allclose(result, expected)

def test_loaded_image_scalar_bands_preserve_pixel_shape(self, av3c_image):
image = av3c_image.load()
values = np.asarray(image)
red = values[:, :, 27].astype(float)
nir = values[:, :, 47].astype(float)

result = ndvi(image, 27, 47)

assert result.shape == image.shape[:2]
assert_allclose(np.asarray(result), (nir - red) / (nir + red))


class TestBdist:

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