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Formatting w/ black
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@mojodna
mojodna committed Apr 27, 2018
1 parent 5d3eb41 commit 4981618
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Showing 2 changed files with 103 additions and 61 deletions.
159 changes: 98 additions & 61 deletions marblecutter/recipes.py
View file
Expand Up @@ -13,12 +13,7 @@

from .utils import PixelCollection, Source

BAND_MAPPING = {
"r": 0,
"g": 1,
"b": 2,
"pan": 4,
}
BAND_MAPPING = {"r": 0, "g": 1, "b": 2, "pan": 4}
LOG = logging.getLogger(__name__)


Expand All @@ -34,37 +29,45 @@ def apply(recipes, pixels, source=None):

for bdx, source_band in enumerate((4, 3, 2)):
sun_elev = source.meta["L1_METADATA_FILE"]["IMAGE_ATTRIBUTES"][
"SUN_ELEVATION"]
multi_reflect = source.meta[
"L1_METADATA_FILE"]["RADIOMETRIC_RESCALING"].get(
"REFLECTANCE_MULT_BAND_{}".format(source_band))
add_reflect = source.meta[
"L1_METADATA_FILE"]["RADIOMETRIC_RESCALING"].get(
"REFLECTANCE_ADD_BAND_{}".format(source_band))

min_val = source.meta.get("values", {}).get(str(source_band),
{}).get(
"min", dtype_min)
max_val = source.meta.get("values", {}).get(str(source_band),
{}).get(
"max", dtype_max)
"SUN_ELEVATION"
]
multi_reflect = source.meta["L1_METADATA_FILE"][
"RADIOMETRIC_RESCALING"
].get(
"REFLECTANCE_MULT_BAND_{}".format(source_band)
)
add_reflect = source.meta["L1_METADATA_FILE"]["RADIOMETRIC_RESCALING"].get(
"REFLECTANCE_ADD_BAND_{}".format(source_band)
)

min_val = source.meta.get("values", {}).get(str(source_band), {}).get(
"min", dtype_min
)
max_val = source.meta.get("values", {}).get(str(source_band), {}).get(
"max", dtype_max
)

band_data = 10000 * reflectance.reflectance(
data[bdx], multi_reflect, add_reflect, sun_elev, src_nodata=0)
data[bdx], multi_reflect, add_reflect, sun_elev, src_nodata=0
)

# calculate local min/max as fallbacks
if (min_val == dtype_min and max_val == dtype_max
and len(data.compressed()) > 0):
local_min, local_max = np.percentile(band_data.compressed(),
(2, 98))
if (
min_val == dtype_min
and max_val == dtype_max
and len(data.compressed()) > 0
):
local_min, local_max = np.percentile(band_data.compressed(), (2, 98))
min_val = max(min_val, local_min)
max_val = min(max_val, local_max)

out[bdx] = np.ma.where(band_data > 0,
utils.linear_rescale(
band_data,
in_range=[min_val, max_val],
out_range=[0, 1]), 0)
out[bdx] = np.ma.where(
band_data > 0,
utils.linear_rescale(
band_data, in_range=[min_val, max_val], out_range=[0, 1]
),
0,
)

data = out

Expand All @@ -82,23 +85,32 @@ def apply(recipes, pixels, source=None):

for band in xrange(0, data.shape[0]):
min_val = source.meta.get("values", {}).get(band, {}).get(
"min", dtype_min)
"min", dtype_min
)
max_val = source.meta.get("values", {}).get(band, {}).get(
"max", dtype_max)

if (min_val == dtype_min and max_val == dtype_max
and len(data.compressed()) > 0):
"max", dtype_max
)

if (
min_val == dtype_min
and max_val == dtype_max
and len(data.compressed()) > 0
):
local_min, local_max = np.percentile(
data[band].compressed(), (2, 98))
data[band].compressed(), (2, 98)
)
min_val = max(min_val, local_min)
max_val = min(max_val, local_max)

data[band] = np.ma.where(data[band] > 0,
utils.linear_rescale(
data[band],
in_range=[min_val, max_val],
out_range=[dtype_min, dtype_max]),
0)
data[band] = np.ma.where(
data[band] > 0,
utils.linear_rescale(
data[band],
in_range=[min_val, max_val],
out_range=[dtype_min, dtype_max],
),
0,
)

if data.shape[0] == 1:
# likely greyscale image; use the same band on all channels
Expand All @@ -123,10 +135,16 @@ def preprocess(sources, resolution=None):
for target_band, source_band in iter(source.band_info.items()):
band = BAND_MAPPING.get(target_band)
s = Source(
source.url.replace("{band}",
str(source_band)), source.name,
source.resolution, source.band_info, source.meta,
source.recipes, source.acquired_at, band, source.priority)
source.url.replace("{band}", str(source_band)),
source.name,
source.resolution,
source.band_info,
source.meta,
source.recipes,
source.acquired_at,
band,
source.priority,
)
if target_band == "pan":
if min(resolution) < source.resolution * 2:
yield s
Expand Down Expand Up @@ -155,34 +173,52 @@ def postprocess(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])])
[
(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)))))
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)
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))
ws = filter(
lambda sw: is_rgb(sw[0].band), landsat_windows.pop(scene_id)
)
canvas_data = np.ma.zeros(
(3, ) + pixels.data.shape[1:],
(3,) + pixels.data.shape[1:],
dtype=pixels.data.dtype,
fill_value=_nodata(np.int16))
fill_value=_nodata(np.int16),
)
canvas_data.mask = True

canvas = PixelCollection(canvas_data, pixels.bounds)
Expand All @@ -191,8 +227,9 @@ def postprocess(windows):
if scene_id in pan_bands:
pan = pan_bands[scene_id]

pansharpened, _ = Brovey(pixels.data, pan.data[0], 0.2,
pan.data.dtype)
pansharpened, _ = Brovey(
pixels.data, pan.data[0], 0.2, pan.data.dtype
)

yield source, PixelCollection(pansharpened, pixels.bounds)
else:
Expand Down
5 changes: 5 additions & 0 deletions pylama.ini
View file
@@ -0,0 +1,5 @@
[pylama:pep8]
max_line_length = 88

[pylama:pycodestyle]
max_line_length = 88

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