def pansharpen(vis, vis_transform, pan, pan_transform,
pan_dtype, r_crs, dst_crs, weight,
method="Brovey", src_nodata=0):
"""Pansharpen a lower-resolution visual band
Parameters
=========
vis: ndarray, 3D with shape == (3, vh, vw)
Visual band array with RGB bands
vis_transform: Affine
affine transform defining the georeferencing of the vis array
pan: ndarray, 2D with shape == (ph, pw)
Panchromatic band array
pan_transform: Affine
affine transform defining the georeferencing of the pan array
method: string
Algorithm for pansharpening; default Brovey
Returns:
======
pansharp: ndarray, 3D with shape == (3, ph, pw)
pansharpened visual band
affine transform is identical to `pan_transform`
"""
rgb = _upsample(_create_apply_mask(vis), pan.shape, vis_transform, r_crs,
pan_transform, dst_crs)
# Main Pansharpening Processing
if method == "Brovey":
pansharp, _ = Brovey(rgb, pan, weight, pan_dtype)
# TODO: add other methods
return pansharp
def postprocess(windows):
from . import _nodata
windows = list(filter(None, windows))
landsat_windows = filter(lambda sw: "LC08_" in sw[0].url, windows)
landsat_windows = dict([(k, list(v)) for k, v in itertools.groupby(
landsat_windows, lambda x: x[0].url.split("/")[-2])])
pan_bands = dict(
list(
map(
lambda sw: (sw[0].url.split("/")[-2], sw[1]),
filter(lambda sw: sw[0].band == 4, filter(None, windows)),
)))
for source, pixels in windows:
if pixels is None or pixels.data is None:
continue
if "landsat8" in source.recipes:
scene_id = source.url.split("/")[-2]
source = Source(
"/".join(source.url.split("/")[0:-1]),
source.name,
source.resolution,
source.band_info,
source.meta,
source.recipes,
source.acquired_at,
None,
source.priority,
source.coverage,
)
# pick out all bands for the same scene the first time it's seen
if scene_id in landsat_windows:
ws = filter(lambda sw: is_rgb(sw[0].band),
landsat_windows.pop(scene_id))
canvas_data = np.ma.zeros(
(3, ) + pixels.data.shape[1:],
dtype=pixels.data.dtype,
fill_value=_nodata(np.int16),
)
canvas_data.mask = True
canvas = PixelCollection(canvas_data, pixels.bounds)
pixels = reduce(_reduce_landsat_windows, ws, canvas)
if scene_id in pan_bands:
pan = pan_bands[scene_id]
pansharpened, _ = Brovey(pixels.data, pan.data[0], 0.2,
pan.data.dtype)
yield source, PixelCollection(pansharpened, pixels.bounds)
else:
yield source, pixels
else:
yield source, pixels
def test_Brovey(rgb, pan, weight, pan_dtype):
brovey_output, brovey_ratio = Brovey(rgb, pan, weight, pan_dtype)
ratio = pan / ((rgb[0] + rgb[1] + rgb[2] * weight) / (2 + weight))
output = np.clip(ratio * rgb, 0, np.iinfo(pan_dtype).max).astype(pan_dtype)
assert np.array_equal(brovey_output, output)
assert np.array_equal(brovey_ratio, ratio)