def __init__(self, uri):
try:
if uri.startswith("s3://"):
url = urlparse(uri)
obj = S3.get_object(Bucket=url.netloc, Key=url.path[1:])
oin_meta = json.loads(obj["Body"].read().decode("utf-8"))
elif uri.startswith(("http://", "https://")):
oin_meta = requests.get(uri).json()
else:
raise NoCatalogAvailable()
except Exception:
raise NoCatalogAvailable()
self._meta = oin_meta
self._metadata_url = uri
self._name = oin_meta.get("title")
self._provider = oin_meta.get("provider")
self._source = oin_meta.get("uuid")
with get_source(self._source) as src:
self._bounds = warp.transform_bounds(src.crs, WGS84_CRS,
*src.bounds)
self._resolution = get_resolution_in_meters(
Bounds(src.bounds, src.crs), (src.height, src.width))
approximate_zoom = get_zoom(max(self._resolution), op=math.ceil)
if src.meta["dtype"] != "uint8":
global_min = src.get_tag_item("TIFFTAG_MINSAMPLEVALUE")
global_max = src.get_tag_item("TIFFTAG_MAXSAMPLEVALUE")
for band in range(0, src.count):
self._meta["values"] = self._meta.get("values", {})
self._meta["values"][band] = {}
min_val = src.get_tag_item("STATISTICS_MINIMUM",
bidx=band + 1)
max_val = src.get_tag_item("STATISTICS_MAXIMUM",
bidx=band + 1)
mean_val = src.get_tag_item("STATISTICS_MEAN",
bidx=band + 1)
if min_val is not None:
self._meta["values"][band]["min"] = float(min_val)
elif global_min is not None:
self._meta["values"][band]["min"] = float(global_min)
if max_val is not None:
self._meta["values"][band]["max"] = float(max_val)
elif global_max is not None:
self._meta["values"][band]["max"] = float(global_max)
if mean_val is not None:
self._meta["values"][band]["mean"] = float(mean_val)
self._center = [
(self._bounds[0] + self.bounds[2]) / 2,
(self._bounds[1] + self.bounds[3]) / 2,
approximate_zoom - 3,
]
self._maxzoom = approximate_zoom + 3
self._minzoom = approximate_zoom - 10
def __init__(self, uri):
rsp = requests.get(uri)
if not rsp.ok:
raise NoDataAvailable()
oin_meta = rsp.json()
self._meta = oin_meta
self._metadata_url = uri
self._name = oin_meta.get('title')
self._provider = oin_meta.get('provider')
self._source = oin_meta.get('uuid')
with get_source(self._source) as src:
self._bounds = warp.transform_bounds(src.crs, WGS84_CRS,
*src.bounds)
self._resolution = get_resolution_in_meters(
Bounds(src.bounds, src.crs), (src.height, src.width))
approximate_zoom = get_zoom(max(self._resolution), op=math.ceil)
self._center = [(self._bounds[0] + self.bounds[2]) / 2,
(self._bounds[1] + self.bounds[3]) / 2,
approximate_zoom - 3]
self._maxzoom = approximate_zoom + 3
self._minzoom = approximate_zoom - 10
def __init__(self, uri, rgb=None, nodata=None, linear_stretch=None, resample=None):
self._uri = uri
if rgb:
self._rgb = rgb
if nodata:
self._nodata = nodata
if linear_stretch:
self._linear_stretch = linear_stretch
try:
# test whether provided resampling method is valid
Resampling[resample]
self._resample = resample
except KeyError:
self._resample = None
self._meta = {}
with get_source(self._uri) as src:
self._bounds = warp.transform_bounds(src.crs, WGS84_CRS, *src.bounds)
self._resolution = get_resolution_in_meters(
Bounds(src.bounds, src.crs), (src.height, src.width)
)
approximate_zoom = get_zoom(max(self._resolution), op=math.ceil)
global_min = src.get_tag_item("TIFFTAG_MINSAMPLEVALUE")
global_max = src.get_tag_item("TIFFTAG_MAXSAMPLEVALUE")
for band in range(0, src.count):
self._meta["values"] = self._meta.get("values", {})
self._meta["values"][band] = {}
min_val = src.get_tag_item("STATISTICS_MINIMUM", bidx=band + 1)
max_val = src.get_tag_item("STATISTICS_MAXIMUM", bidx=band + 1)
mean_val = src.get_tag_item("STATISTICS_MEAN", bidx=band + 1)
if min_val is not None:
self._meta["values"][band]["min"] = float(min_val)
elif global_min is not None:
self._meta["values"][band]["min"] = float(global_min)
if max_val is not None:
self._meta["values"][band]["max"] = float(max_val)
elif global_max is not None:
self._meta["values"][band]["max"] = float(global_max)
if mean_val is not None:
self._meta["values"][band]["mean"] = float(mean_val)
self._center = [
(self._bounds[0] + self.bounds[2]) / 2,
(self._bounds[1] + self.bounds[3]) / 2,
approximate_zoom - 3,
]
self._maxzoom = approximate_zoom + 3
self._minzoom = approximate_zoom - 10
def add_pixels_to_asset(asset):
try:
url = asset['url']
with Timer(f'reading pixels for {url}'):
source = Source(url=url, name=url, resolution=None)
with get_source(url) as src:
with Timer(f'reading window for {url}'):
asset['pixels'] = read_window(
src, canvas_bounds, shape, source)
except Exception as e:
LOG.error(e)
raise e
def __init__(self, uri, rgb=None, nodata=None, linear_stretch=None, resample=None,
dst_max=None, dst_min=None, force_cast=None, to_vis=None):
self._uri = uri
self._rgb = rgb
self._nodata = nodata
self._linear_stretch = linear_stretch
self._dst_min = dst_min
self._dst_max = dst_max
self._force_cast = force_cast
self._to_vis = to_vis
try:
# test whether provided resampling method is valid
Resampling[resample]
self._resample = resample
except KeyError:
self._resample = None
self._meta = {}
self.src_meta = {}
with get_source(self._uri) as src:
self.src_meta = snake_case_to_camel_case_keys_of_dict(src.tags())
self.src_meta["bandCount"] = src.count
self._bounds = warp.transform_bounds(src.crs, WGS84_CRS, *src.bounds)
self._resolution = get_resolution_in_meters(
Bounds(src.bounds, src.crs), (src.height, src.width)
)
approximate_zoom = get_zoom(max(self._resolution), op=math.ceil)
global_min = src.get_tag_item("TIFFTAG_MINSAMPLEVALUE")
global_max = src.get_tag_item("TIFFTAG_MAXSAMPLEVALUE")
band_order = src.get_tag_item("BAND_ORDER")
if band_order is not None:
band_order = band_order.split(',')
if str(self._rgb).lower() == "metadata":
if band_order is not None:
def get_band_from_band_order(band_order, band_name, fallback):
if band_name in band_order:
return str(band_order.index(band_name) + 1)
else:
return fallback
red_band = get_band_from_band_order(band_order, "RED", "1")
green_band = get_band_from_band_order(band_order, "GRE", "2")
blue_band = get_band_from_band_order(band_order, "BLU", "3")
self._rgb = ",".join([red_band, green_band, blue_band])
else:
# Fallback
if src.count >= 3:
self._rgb = "1,2,3"
else:
self._rgb = "1,1,1"
self.src_meta["bandMetadata"] = {}
# FarmLens specific
band_assignments = band_order
if band_order is None:
band_assignments = range(0, src.count)
for band in xrange(0, src.count):
self.src_meta["bandMetadata"][band_assignments[band]] = snake_case_to_camel_case_keys_of_dict(src.tags(bidx=band+1))
self._meta["values"] = self._meta.get("values", {})
self._meta["values"][band] = {}
min_val = src.get_tag_item("STATISTICS_MINIMUM", bidx=band + 1)
max_val = src.get_tag_item("STATISTICS_MAXIMUM", bidx=band + 1)
mean_val = src.get_tag_item("STATISTICS_MEAN", bidx=band + 1)
stddev_val = src.get_tag_item("STATISTICS_STDDEV", bidx=band + 1)
if min_val is not None:
self._meta["values"][band]["min"] = float(min_val)
elif global_min is not None:
self._meta["values"][band]["min"] = float(global_min)
if max_val is not None:
self._meta["values"][band]["max"] = float(max_val)
elif global_max is not None:
self._meta["values"][band]["max"] = float(global_max)
if mean_val is not None:
self._meta["values"][band]["mean"] = float(mean_val)
if stddev_val is not None:
self._meta["values"][band]["stddev"] = float(stddev_val)
self._center = [
(self._bounds[0] + self.bounds[2]) / 2,
(self._bounds[1] + self.bounds[3]) / 2,
approximate_zoom - 3,
]
self._maxzoom = approximate_zoom + 3
self._minzoom = approximate_zoom - 10