def test_sentinel_id_invalid():
"""
Should raise an error with invalid sceneid
"""
scene = 'S2A_tile_20170323_17SNC'
with pytest.raises(InvalidSentinelSceneId):
utils.sentinel_parse_scene_id(scene)
def tile(sceneid,
tile_x,
tile_y,
tile_z,
rgb=('04', '03', '02'),
tilesize=256):
"""Create mercator tile from Sentinel-2 data.
Attributes
----------
sceneid : str
Sentinel-2 sceneid.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
rgb : tuple, str, optional (default: ('04', '03', '02'))
Bands index for the RGB combination.
tilesize : int, optional (default: 256)
Output image size.
Returns
-------
data : numpy ndarray
mask: numpy array
"""
if not isinstance(rgb, tuple):
rgb = tuple((rgb, ))
scene_params = utils.sentinel_parse_scene_id(sceneid)
sentinel_address = '{}/{}'.format(SENTINEL_BUCKET, scene_params['key'])
sentinel_preview = '{}/preview.jp2'.format(sentinel_address)
with rasterio.open(sentinel_preview) as src:
wgs_bounds = transform_bounds(*[src.crs, 'epsg:4326'] +
list(src.bounds),
densify_pts=21)
if not utils.tile_exists(wgs_bounds, tile_z, tile_x, tile_y):
raise TileOutsideBounds('Tile {}/{}/{} is outside image bounds'.format(
tile_z, tile_x, tile_y))
mercator_tile = mercantile.Tile(x=tile_x, y=tile_y, z=tile_z)
tile_bounds = mercantile.xy_bounds(mercator_tile)
addresses = ['{}/B{}.jp2'.format(sentinel_address, band) for band in rgb]
_tiler = partial(utils.tile_band_worker,
bounds=tile_bounds,
tilesize=tilesize,
nodata=0)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data, masks = zip(*list(executor.map(_tiler, addresses)))
mask = np.all(masks, axis=0).astype(np.uint8) * 255
return np.concatenate(data), mask
def bounds(sceneid):
"""Retrieve image bounds.
Attributes
----------
sceneid : str
Sentinel-2 sceneid.
Returns
-------
out : dict
dictionary with image bounds.
"""
scene_params = utils.sentinel_parse_scene_id(sceneid)
sentinel_address = '{}/{}'.format(SENTINEL_BUCKET, scene_params['key'])
with rasterio.open('{}/preview.jp2'.format(sentinel_address)) as src:
wgs_bounds = transform_bounds(*[src.crs, 'epsg:4326'] +
list(src.bounds),
densify_pts=21)
info = {'sceneid': sceneid}
info['bounds'] = list(wgs_bounds)
return info
def area(
scene,
bbox,
expression,
expression_range=[-1, 1],
bbox_crs="epsg:4326",
out_crs="epsg:3857",
):
"""Area handler."""
max_img_size = 512
bands = tuple(set(re.findall(r"b(?P<bands>[0-9]{1,2})", expression)))
scene_params = sentinel_parse_scene_id(scene)
sentinel_address = f'{SENTINEL_BUCKET}/{scene_params["key"]}'
scene_info = sentinel2_get_info(os.path.basename(SENTINEL_BUCKET),
scene_params["key"],
request_pays=True)
addresses = [f"{sentinel_address}/B{band}.jp2" for band in bands]
_worker = partial(
get_area,
bbox=bbox,
max_img_size=max_img_size,
bbox_crs=bbox_crs,
out_crs=out_crs,
)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data = np.concatenate(list(executor.map(_worker, addresses)))
if not np.any(data):
raise Exception("No valid data in array")
mask = np.all(data != 0, axis=0).astype(np.uint8) * 255
ctx = {}
for bdx, b in enumerate(bands):
ctx["b{}".format(b)] = data[bdx]
ratio = np.nan_to_num(ne.evaluate(expression, local_dict=ctx))
ratio = np.where(
mask,
linear_rescale(ratio, in_range=expression_range, out_range=[0, 255]),
0).astype(np.uint8)
img = array_to_img(ratio, mask, get_colormap(name="cfastie"))
ndvi = b64_encode_img(img, "jpeg")
date = (scene_params["acquisitionYear"] + "-" +
scene_params["acquisitionMonth"] + "-" +
scene_params["acquisitionDay"])
return {
"ndvi": ndvi,
"date": date,
"sat": scene_info["sat"],
"scene": scene,
"cloud": scene_info["cloud_coverage"],
}
def metadata(sceneid, pmin=2, pmax=98):
"""
Retrieve image bounds and histogram info.
Attributes
----------
sceneid : str
Sentinel-2 sceneid.
pmin : int, optional, (default: 2)
Histogram minimum cut.
pmax : int, optional, (default: 98)
Histogram maximum cut.
Returns
-------
out : dict
dictionary with image bounds and bands histogram cuts.
"""
scene_params = utils.sentinel_parse_scene_id(sceneid)
sentinel_address = "{}/{}".format(SENTINEL_BUCKET, scene_params["key"])
with rasterio.open("{}/preview.jp2".format(sentinel_address)) as src:
wgs_bounds = transform_bounds(*[src.crs, "epsg:4326"] +
list(src.bounds),
densify_pts=21)
info = {"sceneid": sceneid, "bounds": list(wgs_bounds)}
bands = [
"01",
"02",
"03",
"04",
"05",
"06",
"07",
"08",
"8A",
"09",
"10",
"11",
"12",
]
addresses = [
"{}/preview/B{}.jp2".format(sentinel_address, band) for band in bands
]
_min_max_worker = partial(utils.band_min_max_worker, pmin=pmin, pmax=pmax)
with futures.ThreadPoolExecutor(max_workers=2) as executor:
responses = list(executor.map(_min_max_worker, addresses))
info["rgbMinMax"] = dict(zip(bands, responses))
return info
def point(scene, coordinates, expression):
"""Point handler."""
bands = tuple(set(re.findall(r"b(?P<bands>[0-9]{1,2})", expression)))
scene_params = sentinel_parse_scene_id(scene)
sentinel_address = f'{SENTINEL_BUCKET}/{scene_params["key"]}'
scene_info = sentinel2_get_info(os.path.basename(SENTINEL_BUCKET),
scene_params["key"],
request_pays=True)
addresses = [f"{sentinel_address}/B{band}.jp2" for band in bands]
def worker(address):
"""Worker."""
with rasterio.open(address) as band:
lon_srs, lat_srs = warp.transform("EPSG:4326", band.crs,
[coordinates[0]],
[coordinates[1]])
point = list(band.sample([(lon_srs[0], lat_srs[0])]))[0]
return point[0]
try:
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data = list(executor.map(worker, addresses))
ctx = {}
for bdx, b in enumerate(bands):
ctx["b{}".format(b)] = data[bdx]
ratio = np.nan_to_num(ne.evaluate(expression, local_dict=ctx))
except IndexError:
ratio = 0.0
date = (scene_params["acquisitionYear"] + "-" +
scene_params["acquisitionMonth"] + "-" +
scene_params["acquisitionDay"])
return {
"ndvi": ratio,
"date": date,
"sat": scene_info["sat"],
"scene": scene,
"cloud": scene_info["cloud_coverage"],
}
def metadata(sceneid, pmin=2, pmax=98):
"""Retrieve image bounds and histogram info.
Attributes
----------
sceneid : str
Sentinel-2 sceneid.
pmin : int, optional, (default: 2)
Histogram minimum cut.
pmax : int, optional, (default: 98)
Histogram maximum cut.
Returns
-------
out : dict
dictionary with image bounds and bands histogram cuts.
"""
scene_params = utils.sentinel_parse_scene_id(sceneid)
sentinel_address = '{}/{}'.format(SENTINEL_BUCKET, scene_params['key'])
with rasterio.open('{}/preview.jp2'.format(sentinel_address)) as src:
wgs_bounds = transform_bounds(*[src.crs, 'epsg:4326'] +
list(src.bounds),
densify_pts=21)
info = {'sceneid': sceneid, 'bounds': list(wgs_bounds)}
bands = [
'01', '02', '03', '04', '05', '06', '07', '08', '8A', '09', '10', '11',
'12'
]
addresses = [
'{}/preview/B{}.jp2'.format(sentinel_address, band) for band in bands
]
_min_max_worker = partial(utils.band_min_max_worker, pmin=pmin, pmax=pmax)
with futures.ThreadPoolExecutor(max_workers=2) as executor:
responses = list(executor.map(_min_max_worker, addresses))
info['rgbMinMax'] = dict(zip(bands, responses))
return info
def test_sentinel_id_valid_strip():
"""
Should work as expected (parse sentinel scene id)
"""
scene = 'S2A_tile_20170323_07SNC_0'
expected_content = {
'acquisitionDay': '23',
'acquisitionMonth': '03',
'acquisitionYear': '2017',
'key': 'tiles/7/S/NC/2017/3/23/0',
'lat': 'NC',
'num': '0',
'satellite': 'A',
'sensor': '2',
'sq': 'S',
'utm': '07'
}
assert utils.sentinel_parse_scene_id(scene) == expected_content
def tile(sceneid, tile_x, tile_y, tile_z, rgb=('04', '03', '02'),
r_bds=(0, 16000), g_bds=(0, 16000), b_bds=(1, 16000), tilesize=256):
"""Create mercator tile from Sentinel-2 data and encodes it in base64.
Attributes
----------
sceneid : str
Sentinel-2 sceneid.
tile_x : int
Mercator tile X index.
tile_y : int
Mercator tile Y index.
tile_z : int
Mercator tile ZOOM level.
rgb : tuple, int, optional (default: ('04', '03', '02'))
Bands index for the RGB combination.
r_bds : tuple, int, optional (default: (0, 16000))
First band (red) DN min and max values (DN * 10,000)
used for the linear rescaling.
g_bds : tuple, int, optional (default: (0, 16000))
Second band (green) DN min and max values (DN * 10,000)
used for the linear rescaling.
b_bds : tuple, int, optional (default: (0, 16000))
Third band (blue) DN min and max values (DN * 10,000)
used for the linear rescaling.
tilesize : int, optional (default: 256)
Output image size.
Returns
-------
out : numpy ndarray (type: uint8)
"""
scene_params = utils.sentinel_parse_scene_id(sceneid)
sentinel_address = '{}/{}'.format(SENTINEL_BUCKET, scene_params['key'])
sentinel_preview = '{}/preview.jp2'.format(sentinel_address)
with rasterio.open(sentinel_preview) as src:
wgs_bounds = transform_bounds(
*[src.crs, 'epsg:4326'] + list(src.bounds), densify_pts=21)
if not utils.tile_exists(wgs_bounds, tile_z, tile_x, tile_y):
raise TileOutsideBounds('Tile {}/{}/{} is outside image bounds'.format(
tile_z, tile_x, tile_y))
mercator_tile = mercantile.Tile(x=tile_x, y=tile_y, z=tile_z)
tile_bounds = mercantile.xy_bounds(mercator_tile)
# define a list of bands Min and Max Values (form input)
histo_cuts = dict(zip(rgb, [r_bds, g_bds, b_bds]))
addresses = ['{}/B{}.jp2'.format(sentinel_address, band) for band in rgb]
_tiler = partial(utils.tile_band_worker,
bounds=tile_bounds,
tilesize=tilesize)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
out = np.stack(executor.map(_tiler, addresses))
for bdx, band in enumerate(rgb):
# Rescale Intensity to byte (1->255) with 0 being NoData
out[bdx] = np.where(
out[bdx] > 0,
utils.linear_rescale(out[bdx],
in_range=histo_cuts.get(band),
out_range=[1, 255]), 0)
return out.astype(np.uint8)
