def test_array_to_img_invalid_format():
"""Should raise an error with invalid format
"""
arr = np.random.randint(0, 255, size=(1, 512, 512), dtype=np.uint8)
img = utils.array_to_img(arr)
with pytest.raises(InvalidFormat):
utils.b64_encode_img(img, 'gif')
def ratio(scene, tile_z, tile_x, tile_y, tileformat):
"""Handle processing requests
"""
if tileformat == 'jpg':
tileformat = 'jpeg'
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
ratio_value = query_args['ratio']
range_value = query_args.get('range', [-1, 1])
tilesize = query_args.get('tile', 256)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
tile, mask = expression(scene,
tile_x,
tile_y,
tile_z,
ratio_value,
tilesize=tilesize)
if len(tile.shape) == 2:
tile = np.expand_dims(tile, axis=0)
rtile = np.where(
mask, linear_rescale(tile, in_range=range_value, out_range=[0, 255]),
0).astype(np.uint8)
img = array_to_img(rtile,
color_map=get_colormap(name='cfastie'),
mask=mask)
str_img = b64_encode_img(img, tileformat)
return ('OK', f'image/{tileformat}', str_img)
def tile(scene, tile_z, tile_x, tile_y, tileformat):
"""Handle tile requests
"""
if tileformat == 'jpg':
tileformat = 'jpeg'
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
bands = query_args.get('rgb', '4,3,2')
bands = tuple(re.findall(r'\d+', bands))
histoCut = query_args.get('histo', ';'.join(['0,16000'] * len(bands)))
histoCut = re.findall(r'\d+,\d+', histoCut)
histoCut = list(map(lambda x: list(map(int, x.split(','))), histoCut))
if len(bands) != len(histoCut):
raise LandsatTilerError('The number of bands doesn\'t match the number of histogramm values')
tilesize = query_args.get('tile', 256)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
pan = True if query_args.get('pan') else False
tile, mask = landsat8.tile(scene, tile_x, tile_y, tile_z, bands, pan=pan, tilesize=tilesize)
rtile = np.zeros((len(bands), tilesize, tilesize), dtype=np.uint8)
for bdx in range(len(bands)):
rtile[bdx] = np.where(mask, linear_rescale(tile[bdx], in_range=histoCut[bdx], out_range=[0, 255]), 0)
img = array_to_img(rtile, mask=mask)
str_img = b64_encode_img(img, tileformat)
return ('OK', f'image/{tileformat}', str_img)
def tile(tile_z, tile_x, tile_y, tileformat):
"""Handle tile requests."""
if tileformat == 'jpg':
tileformat = 'jpeg'
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
address = query_args['url']
indexes = query_args.get('indexes')
if indexes:
indexes = tuple(int(s) for s in re.findall(r'\d+', indexes))
tilesize = query_args.get('tile', 512)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
nodata = query_args.get('nodata')
if nodata is not None:
nodata = int(nodata)
tile, mask = main.tile(address,
tile_x,
tile_y,
tile_z,
indexes=indexes,
tilesize=tilesize,
nodata=nodata)
img = array_to_img(tile, mask=mask)
str_img = b64_encode_img(img, tileformat)
return ('OK', f'image/{tileformat}', str_img)
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 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 = landsat_parse_scene_id(scene)
meta_data = landsat_get_mtl(scene).get("L1_METADATA_FILE")
landsat_address = f'{LANDSAT_BUCKET}/{scene_params["key"]}'
def worker(band):
"""Worker."""
address = f"{landsat_address}_B{band}.TIF"
sun_elev = meta_data["IMAGE_ATTRIBUTES"]["SUN_ELEVATION"]
multi_reflect = meta_data["RADIOMETRIC_RESCALING"][
f"REFLECTANCE_MULT_BAND_{band}"
]
add_reflect = meta_data["RADIOMETRIC_RESCALING"][f"REFLECTANCE_ADD_BAND_{band}"]
band = get_area(
address, bbox, max_img_size=max_img_size, bbox_crs=bbox_crs, out_crs=out_crs
)
return reflectance(band, multi_reflect, add_reflect, sun_elev, src_nodata=0)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data = np.concatenate(list(executor.map(worker, bands)))
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")
return {
"ndvi": ndvi,
"date": scene_params["date"],
"scene": scene,
"cloud": meta_data["IMAGE_ATTRIBUTES"]["CLOUD_COVER"],
}
def tile(tile_z, tile_x, tile_y, tileformat):
"""Handle tile requests."""
if tileformat == 'jpg':
tileformat = 'jpeg'
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
address = query_args['url']
## Read Stac File
with urllib.request.urlopen(address) as url:
data = json.loads(url.read().decode())
asset_key = query_args.get('asset_key', 'raster')
raster_address = data['assets'][asset_key]['href']
indexes = query_args.get('indexes')
if indexes:
indexes = tuple(int(s) for s in re.findall(r'\d+', indexes))
tilesize = query_args.get('tile', 512)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
nodata = query_args.get('nodata')
if nodata is not None:
nodata = int(nodata)
raster_address = data['assets'][asset_key]['href']
tile, mask = main.tile(raster_address,
tile_x,
tile_y,
tile_z,
indexes=indexes,
tilesize=tilesize,
nodata=nodata)
linearStretch = query_args.get('linearStretch')
if linearStretch is not None:
if util.strtobool(linearStretch):
tile = linear_rescale(tile, in_range=(np.min(tile), np.max(tile)))
img = array_to_img(tile, mask=mask)
str_img = b64_encode_img(img, tileformat)
return ('OK', f'image/{tileformat}', str_img)
def tile(scene, tile_z, tile_x, tile_y, tileformat):
"""Handle tile requests."""
if tileformat == "jpg":
tileformat = "jpeg"
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
bands = query_args.get("rgb", "04,03,02")
bands = tuple(re.findall(r"[0-9A]{2}", bands))
histoCut = query_args.get("histo", "-".join(["0,16000"] * len(bands)))
histoCut = re.findall(r"\d+,\d+", histoCut)
histoCut = list(map(lambda x: list(map(int, x.split(","))), histoCut))
if len(bands) != len(histoCut):
raise SentinelTilerError(
"The number of bands doesn't match the number of histogramm values"
)
tilesize = query_args.get("tile", 256)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
tile, mask = sentinel2.tile(scene,
tile_x,
tile_y,
tile_z,
bands,
tilesize=tilesize)
rtile = np.zeros((len(bands), tilesize, tilesize), dtype=np.uint8)
for bdx in range(len(bands)):
rtile[bdx] = np.where(
mask,
linear_rescale(tile[bdx],
in_range=histoCut[bdx],
out_range=[0, 255]),
0,
)
img = array_to_img(rtile, mask=mask)
str_img = b64_encode_img(img, tileformat)
return ("OK", f"image/{tileformat}", str_img)
def create(
scene,
bands=None,
expression=None,
expression_range=[-1, 1],
img_format="jpeg",
ovrSize=512,
):
"""Handler."""
if img_format not in ["png", "jpeg"]:
raise UserWarning(f"Invalid {img_format} extension")
if not expression and not bands:
raise Exception("Expression or Bands must be provided")
if bands:
nb_bands = len(bands)
if nb_bands != 3:
raise Exception("RGB combination only")
if expression:
bands = tuple(set(re.findall(r"b(?P<bands>[0-9]{1,2})", expression)))
rgb = expression.split(",")
nb_bands = len(rgb)
scene_params = landsat_parse_scene_id(scene)
meta_data = landsat_get_mtl(scene).get("L1_METADATA_FILE")
landsat_address = f'{LANDSAT_BUCKET}/{scene_params["key"]}'
_worker = partial(worker,
landsat_address=landsat_address,
meta=meta_data,
ovr_size=ovrSize)
with futures.ThreadPoolExecutor(max_workers=3) as executor:
data = np.concatenate(list(executor.map(_worker, bands)))
mask = np.all(data != 0, axis=0).astype(np.uint8) * 255
if expression:
ctx = {}
for bdx, b in enumerate(bands):
ctx["b{}".format(b)] = data[bdx]
data = np.array([
np.nan_to_num(ne.evaluate(bloc.strip(), local_dict=ctx))
for bloc in rgb
])
for band in range(data.shape[0]):
imgRange = (expression_range if expression else np.percentile(
data[band][mask > 0], (2, 98)).tolist())
data[band] = np.where(
mask,
linear_rescale(data[band],
in_range=imgRange,
out_range=[0, 255]),
0,
)
data = data.squeeze()
colormap = None if len(data.shape) >= 3 else get_colormap(name="cfastie")
img = array_to_img(data, mask, colormap)
return b64_encode_img(img, img_format)
def test_b64_encode_img_valid_webp():
"""Should work as expected
"""
arr = np.random.randint(0, 255, size=(4, 512, 512), dtype=np.uint8)
img = utils.array_to_img(arr)
assert utils.b64_encode_img(img, 'webp')
def tile(scene, tile_z, tile_x, tile_y, tileformat):
if tileformat == 'jpg':
tileformat = 'jpeg'
query_args = APP.current_request.query_params
query_args = query_args if isinstance(query_args, dict) else {}
bands = query_args.get('rgb', '4,3,2')
label_id = -1
if bands == '12,12,12':
label_id = 0
bands = '4,3,2'
elif bands == '13,13,13':
label_id = 1
bands = '4,3,2'
elif bands == '14,14,14':
label_id = 2
bands = '4,3,2'
elif bands == '15,15,15':
label_id = 3
bands = '4,3,2'
bands = tuple(re.findall(r'\d+', bands))
histoCut = query_args.get('histo', ';'.join(['0,16000'] * len(bands)))
histoCut = re.findall(r'\d+,\d+', histoCut)
histoCut = list(map(lambda x: list(map(int, x.split(','))), histoCut))
if len(bands) != len(histoCut):
raise LandsatTilerError(
'The number of bands doesn\'t match the number of histogramm values'
)
tilesize = query_args.get('tile', 256)
tilesize = int(tilesize) if isinstance(tilesize, str) else tilesize
pan = True if query_args.get('pan') else False
tile, mask = landsat8.tile(scene,
tile_x,
tile_y,
tile_z,
bands,
pan=pan,
tilesize=tilesize)
rtile = np.zeros((len(bands), tilesize, tilesize), dtype=np.uint8)
for bdx in range(len(bands)):
rtile[bdx] = np.where(
mask,
linear_rescale(tile[bdx],
in_range=histoCut[bdx],
out_range=[0, 255]), 0)
img = array_to_img(rtile, mask=mask)
str_img = b64_encode_img(img, tileformat)
if label_id != -1:
data = {
'label_id': label_id,
'img': str_img,
}
r = requests.post(f"http://kursach.ngrok.io/getmask/", json=data)
str_img = r.json()['mask']
img = Image.open(io.BytesIO(base64.decodebytes(eval(f"b'{str_img}'"))))
img_np = np.asarray(img).T
img = array_to_img(img_np, mask=mask)
str_img = b64_encode_img(img, tileformat)
return ('OK', f'image/{tileformat}', str_img)
