def vector_get_attribute_table(
vector: Union[str, ogr.DataSource],
process_layer: int = 0,
include_geom: bool = False,
) -> pd.DataFrame:
type_check(vector, [str, ogr.DataSource], "vector")
type_check(process_layer, [int], "process_layer")
type_check(include_geom, [bool], "include_geom")
ref = open_vector(vector)
metadata = internal_vector_to_metadata(
ref, process_layer=process_layer, create_geometry=False
)
attribute_table_header = None
feature_count = None
attribute_table_header = metadata["layers"][process_layer]["field_names"]
feature_count = metadata["layers"][process_layer]["feature_count"]
attribute_table = []
layer = ref.GetLayer(process_layer)
for _ in range(feature_count):
feature = layer.GetNextFeature()
attributes = [feature.GetFID()]
for field_name in attribute_table_header:
attributes.append(feature.GetField(field_name))
if include_geom:
geom_defn = feature.GetGeometryRef()
attributes.append(geom_defn.ExportToIsoWkt())
attribute_table.append(attributes)
attribute_table_header.insert(0, "fid")
if include_geom:
attribute_table_header.append("geom")
df = pd.DataFrame(attribute_table, columns=attribute_table_header)
return df
def internal_singlepart_to_multipart(
vector: Union[str, ogr.DataSource],
out_path: Optional[str] = None,
overwrite: bool = True,
add_index: bool = True,
process_layer: int = -1,
) -> str:
type_check(vector, [str, ogr.DataSource], "vector")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(overwrite, [bool], "overwrite")
type_check(add_index, [bool], "add_index")
type_check(process_layer, [int], "process_layer")
vector_list, path_list = ready_io_vector(vector, out_path, overwrite=overwrite)
ref = open_vector(vector_list[0])
out_name = path_list[0]
out_format = path_to_driver_vector(out_name)
driver = ogr.GetDriverByName(out_format)
overwrite_required(out_name, overwrite)
metadata = internal_vector_to_metadata(ref)
remove_if_overwrite(out_name, overwrite)
destination: ogr.DataSource = driver.CreateDataSource(out_name)
for index, layer_meta in enumerate(metadata["layers"]):
if process_layer != -1 and index != process_layer:
continue
name = layer_meta["layer_name"]
geom = layer_meta["column_geom"]
sql = f"SELECT ST_Collect({geom}) AS geom FROM {name};"
result = ref.ExecuteSQL(sql, dialect="SQLITE")
destination.CopyLayer(result, name, ["OVERWRITE=YES"])
if add_index:
vector_add_index(destination)
destination.FlushCache()
return out_name
def vector_get_fids(
vector: Union[str, ogr.DataSource], process_layer: int = 0
) -> np.ndarray:
type_check(vector, [str, ogr.DataSource], "vector")
type_check(process_layer, [int], "process_layer")
metadata = internal_vector_to_metadata(vector)
features = metadata["layers"][0]["feature_count"]
ref = open_vector(vector)
layer = ref.GetLayer(process_layer)
if layer is None:
raise Exception(f"Requested a non-existing layer: layer_idx={process_layer}")
fid_list = np.empty(features, dtype=int)
for index in range(features):
feature = layer.GetNextFeature()
fid_list[index] = feature.GetFID()
layer.ResetReading()
return fid_list
def merge_vectors(
vectors: List[Union[str, ogr.DataSource]],
out_path: Optional[str] = None,
preserve_fid: bool = True,
) -> str:
"""Merge vectors to a single geopackage."""
type_check(vectors, [list], "vector")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(preserve_fid, [bool], "preserve_fid")
vector_list = to_vector_list(vectors)
out_driver = "GPKG"
out_format = ".gpkg"
out_target = f"/vsimem/clipped_{uuid4().int}{out_format}"
if out_path is not None:
out_target = out_path
out_driver = path_to_driver_vector(out_path)
out_format = path_to_ext(out_path)
driver = ogr.GetDriverByName(out_driver)
merged_ds: ogr.DataSource = driver.CreateDataSource(out_target)
for vector in vector_list:
ref = open_vector(vector)
metadata = internal_vector_to_metadata(ref)
for layer in metadata["layers"]:
name = layer["layer_name"]
merged_ds.CopyLayer(ref.GetLayer(name), name, ["OVERWRITE=YES"])
merged_ds.FlushCache()
return out_target
def internal_clip_vector(
vector: Union[str, ogr.DataSource],
clip_geom: Union[str, ogr.DataSource, gdal.Dataset],
out_path: Optional[str] = None,
process_layer: int = 0,
process_layer_clip: int = 0,
to_extent: bool = False,
target_projection: Optional[Union[str, ogr.DataSource, gdal.Dataset,
osr.SpatialReference, int]] = None,
preserve_fid: bool = True,
) -> str:
"""Clips a vector to a geometry.
Returns:
A clipped ogr.Datasource or the path to one.
"""
type_check(vector, [str, ogr.DataSource], "vector")
type_check(clip_geom, [ogr.DataSource, gdal.Dataset, str, list, tuple],
"clip_geom")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(process_layer, [int], "process_layer")
type_check(process_layer_clip, [int], "process_layer_clip")
type_check(to_extent, [bool], "to_extent")
type_check(
target_projection,
[str, ogr.DataSource, gdal.Dataset, osr.SpatialReference, int],
"target_projection",
allow_none=True,
)
type_check(preserve_fid, [bool], "preserve_fid")
out_format = ".gpkg"
out_target = f"/vsimem/clipped_{uuid4().int}{out_format}"
if out_path is not None:
out_target = out_path
out_format = path_to_driver_vector(out_path)
options = []
geometry_to_clip = None
if is_vector(clip_geom):
if to_extent:
extent = internal_vector_to_metadata(
clip_geom, create_geometry=True)["extent_datasource"]
geometry_to_clip = internal_vector_to_memory(extent)
else:
geometry_to_clip = open_vector(clip_geom, layer=process_layer_clip)
elif is_raster(clip_geom):
extent = internal_raster_to_metadata(
clip_geom, create_geometry=True)["extent_datasource"]
geometry_to_clip = internal_vector_to_memory(extent)
else:
raise ValueError(
f"Invalid input in clip_geom, unable to parse: {clip_geom}")
clip_vector_path = internal_vector_to_metadata(geometry_to_clip)["path"]
options.append(f"-clipsrc {clip_vector_path}")
if preserve_fid:
options.append("-preserve_fid")
else:
options.append("-unsetFid")
out_projection = None
if target_projection is not None:
out_projection = parse_projection(target_projection, return_wkt=True)
options.append(f"-t_srs {out_projection}")
origin = open_vector(vector, layer=process_layer)
# dst # src
success = gdal.VectorTranslate(
out_target,
get_vector_path(origin),
format=out_format,
options=" ".join(options),
)
if success != 0:
return out_target
else:
raise Exception("Error while clipping geometry.")
def extract_patches(
raster: Union[List[Union[str, gdal.Dataset]], str, gdal.Dataset],
out_dir: Optional[str] = None,
prefix: str = "",
postfix: str = "_patches",
size: int = 32,
offsets: Union[list, None] = [],
generate_border_patches: bool = True,
generate_zero_offset: bool = True,
generate_grid_geom: bool = True,
clip_geom: Optional[Union[str, ogr.DataSource, gdal.Dataset]] = None,
clip_layer_index: int = 0,
verify_output=True,
verification_samples=100,
overwrite=True,
epsilon: float = 1e-9,
verbose: int = 1,
) -> tuple:
"""Extracts square tiles from a raster.
Args:
raster (list of rasters | path | raster): The raster(s) to convert.
**kwargs:
out_dir (path | none): Folder to save output. If None, in-memory
arrays and geometries are outputted.
prefix (str): A prefix for all outputs.
postfix (str): A postfix for all outputs.
size (int): The size of the tiles in pixels.
offsets (list of tuples): List of offsets to extract. Example:
offsets=[(16, 16), (16, 0), (0, 16)]. Will offset the initial raster
and extract from there.
generate_border_patches (bool): The tiles often do not align with the
rasters which means borders are trimmed somewhat. If generate_border_patches
is True, an additional tile is added where needed.
generate_zero_offset (bool): if True, an offset is inserted at (0, 0)
if none is present.
generate_grid_geom (bool): Output a geopackage with the grid of tiles.
clip_geom (str, raster, vector): Clip the output to the
intersections with a geometry. Useful if a lot of the target
area is water or similar.
epsilon (float): How much for buffer the arange array function. This
should usually just be left alone.
verbose (int): If 1 will output messages on progress.
Returns:
A tuple with paths to the generated items. (numpy_array, grid_geom)
"""
type_check(raster, [str, list, gdal.Dataset], "raster")
type_check(out_dir, [str], "out_dir", allow_none=True)
type_check(prefix, [str], "prefix")
type_check(postfix, [str], "postfix")
type_check(size, [int], "size")
type_check(offsets, [list], "offsets", allow_none=True)
type_check(generate_grid_geom, [bool], "generate_grid_geom")
type_check(
clip_geom,
[str, ogr.DataSource, gdal.Dataset],
"clip_layer_index",
allow_none=True,
)
type_check(clip_layer_index, [int], "clip_layer_index")
type_check(overwrite, [bool], "overwrite")
type_check(epsilon, [float], "epsilon")
type_check(verbose, [int], "verbose")
in_rasters = to_raster_list(raster)
if out_dir is not None and not os.path.isdir(out_dir):
raise ValueError(f"Output directory does not exists: {out_dir}")
if not rasters_are_aligned(in_rasters):
raise ValueError(
"Input rasters must be aligned. Please use the align function.")
output_geom = None
metadata = internal_raster_to_metadata(in_rasters[0])
if verbose == 1:
print("Generating blocks..")
# internal offset array. Avoid manipulating the og array.
if offsets is None:
offsets = []
in_offsets = []
if generate_zero_offset and (0, 0) not in offsets:
in_offsets.append((0, 0))
for offset in offsets:
if offset != (0, 0):
if not isinstance(offset, (list, tuple)) or len(offset) != 2:
raise ValueError(
f"offset must be a list or tuple of two integers. Recieved: {offset}"
)
in_offsets.append((offset[0], offset[1]))
border_patches_needed_x = True
border_patches_needed_y = True
if clip_geom is not None:
border_patches_needed_x = False
border_patches_needed_y = False
shapes = []
for offset in in_offsets:
block_shape = shape_to_blockshape(metadata["shape"], (size, size),
offset)
if block_shape[0] * size == metadata["width"]:
border_patches_needed_x = False
if block_shape[1] * size == metadata["height"]:
border_patches_needed_y = False
shapes.append(block_shape)
if generate_border_patches:
cut_x = (metadata["width"] - in_offsets[0][0]) - (shapes[0][0] * size)
cut_y = (metadata["height"] - in_offsets[0][1]) - (shapes[0][1] * size)
if border_patches_needed_x and cut_x > 0:
shapes[0][0] += 1
if border_patches_needed_y and cut_y > 0:
shapes[0][1] += 1
# calculate the offsets
all_rows = 0
offset_rows = []
for i in range(len(shapes)):
row = 0
for j in range(len(shapes[i])):
if j == 0:
row = int(shapes[i][j])
else:
row *= int(shapes[i][j])
offset_rows.append(row)
all_rows += row
offset_rows_cumsum = np.cumsum(offset_rows)
if generate_grid_geom is True or clip_geom is not None:
if verbose == 1:
print("Calculating grid cells..")
mask = np.arange(all_rows, dtype="uint64")
ulx, uly, _lrx, _lry = metadata["extent"]
pixel_width = abs(metadata["pixel_width"])
pixel_height = abs(metadata["pixel_height"])
xres = pixel_width * size
yres = pixel_height * size
dx = xres / 2
dy = yres / 2
# Ready clip geom outside of loop.
if clip_geom is not None:
clip_ref = open_vector(
internal_reproject_vector(clip_geom,
metadata["projection_osr"]))
clip_layer = clip_ref.GetLayerByIndex(clip_layer_index)
meta_clip = internal_vector_to_metadata(clip_ref)
# geom_clip = meta_clip["layers"][clip_layer_index]["column_geom"]
clip_extent = meta_clip["extent_ogr"]
# clip_adjust = [
# clip_extent[0] - clip_extent[0] % xres, # x_min
# (clip_extent[1] - clip_extent[1] % xres) + xres, # x_max
# clip_extent[2] - clip_extent[2] % yres, # y_min
# (clip_extent[3] - clip_extent[3] % yres) + yres, # y_max
# ]
coord_grid = np.empty((all_rows, 2), dtype="float64")
# tiled_extent = [None, None, None, None]
row_count = 0
for idx in range(len(in_offsets)):
x_offset = in_offsets[idx][0]
y_offset = in_offsets[idx][1]
x_step = shapes[idx][0]
y_step = shapes[idx][1]
x_min = (ulx + dx) + (x_offset * pixel_width)
x_max = x_min + (x_step * xres)
y_max = (uly - dy) - (y_offset * pixel_height)
y_min = y_max - (y_step * yres)
# if clip_geom is not None:
# if clip_adjust[0] > x_min:
# x_min = clip_adjust[0] + (x_offset * pixel_width)
# if clip_adjust[1] < x_max:
# x_max = clip_adjust[1] + (x_offset * pixel_width)
# if clip_adjust[2] > y_min:
# y_min = clip_adjust[2] - (y_offset * pixel_height)
# if clip_adjust[3] < y_max:
# y_max = clip_adjust[3] - (y_offset * pixel_height)
# if idx == 0:
# tiled_extent[0] = x_min
# tiled_extent[1] = x_max
# tiled_extent[2] = y_min
# tiled_extent[3] = y_max
# else:
# if x_min < tiled_extent[0]:
# tiled_extent[0] = x_min
# if x_max > tiled_extent[1]:
# tiled_extent[1] = x_max
# if y_min < tiled_extent[2]:
# tiled_extent[2] = y_min
# if y_max > tiled_extent[3]:
# tiled_extent[3] = y_max
# y is flipped so: xmin --> xmax, ymax -- ymin to keep same order as numpy array
x_patches = round((x_max - x_min) / xres)
y_patches = round((y_max - y_min) / yres)
xr = np.arange(x_min, x_max, xres)[0:x_step]
if xr.shape[0] < x_patches:
xr = np.arange(x_min, x_max + epsilon, xres)[0:x_step]
elif xr.shape[0] > x_patches:
xr = np.arange(x_min, x_max - epsilon, xres)[0:x_step]
yr = np.arange(y_max, y_min + epsilon, -yres)[0:y_step]
if yr.shape[0] < y_patches:
yr = np.arange(y_max, y_min - epsilon, -yres)[0:y_step]
elif yr.shape[0] > y_patches:
yr = np.arange(y_max, y_min + epsilon, -yres)[0:y_step]
if generate_border_patches and idx == 0:
if border_patches_needed_x:
xr[-1] = xr[-1] - (
(xr[-1] + dx) - metadata["extent_dict"]["right"])
if border_patches_needed_y:
yr[-1] = yr[-1] - (
(yr[-1] - dy) - metadata["extent_dict"]["bottom"])
oxx, oyy = np.meshgrid(xr, yr)
oxr = oxx.ravel()
oyr = oyy.ravel()
offset_length = oxr.shape[0]
coord_grid[row_count:row_count + offset_length, 0] = oxr
coord_grid[row_count:row_count + offset_length, 1] = oyr
row_count += offset_length
offset_rows_cumsum[idx] = offset_length
offset_rows_cumsum = np.cumsum(offset_rows_cumsum)
coord_grid = coord_grid[:row_count]
# Output geometry
driver = ogr.GetDriverByName("GPKG")
patches_path = f"/vsimem/patches_{uuid4().int}.gpkg"
patches_ds = driver.CreateDataSource(patches_path)
patches_layer = patches_ds.CreateLayer("patches_all",
geom_type=ogr.wkbPolygon,
srs=metadata["projection_osr"])
patches_fdefn = patches_layer.GetLayerDefn()
og_fid = "og_fid"
field_defn = ogr.FieldDefn(og_fid, ogr.OFTInteger)
patches_layer.CreateField(field_defn)
if clip_geom is not None:
clip_feature_count = meta_clip["layers"][clip_layer_index][
"feature_count"]
spatial_index = rtree.index.Index(interleaved=False)
for _ in range(clip_feature_count):
clip_feature = clip_layer.GetNextFeature()
clip_fid = clip_feature.GetFID()
clip_feature_geom = clip_feature.GetGeometryRef()
xmin, xmax, ymin, ymax = clip_feature_geom.GetEnvelope()
spatial_index.insert(clip_fid, (xmin, xmax, ymin, ymax))
fids = 0
mask = []
for tile_id in range(coord_grid.shape[0]):
x, y = coord_grid[tile_id]
if verbose == 1:
progress(tile_id, coord_grid.shape[0], "Patch generation")
x_min = x - dx
x_max = x + dx
y_min = y - dx
y_max = y + dx
tile_intersects = True
grid_geom = None
poly_wkt = None
if clip_geom is not None:
tile_intersects = False
if not ogr_bbox_intersects([x_min, x_max, y_min, y_max],
clip_extent):
continue
intersections = list(
spatial_index.intersection((x_min, x_max, y_min, y_max)))
if len(intersections) == 0:
continue
poly_wkt = f"POLYGON (({x_min} {y_max}, {x_max} {y_max}, {x_max} {y_min}, {x_min} {y_min}, {x_min} {y_max}))"
grid_geom = ogr.CreateGeometryFromWkt(poly_wkt)
for fid1 in intersections:
clip_feature = clip_layer.GetFeature(fid1)
clip_geom = clip_feature.GetGeometryRef()
if grid_geom.Intersects(clip_geom):
tile_intersects = True
continue
if tile_intersects:
ft = ogr.Feature(patches_fdefn)
if grid_geom is None:
poly_wkt = f"POLYGON (({x_min} {y_max}, {x_max} {y_max}, {x_max} {y_min}, {x_min} {y_min}, {x_min} {y_max}))"
grid_geom = ogr.CreateGeometryFromWkt(poly_wkt)
ft_geom = ogr.CreateGeometryFromWkt(poly_wkt)
ft.SetGeometry(ft_geom)
ft.SetField(og_fid, int(fids))
ft.SetFID(int(fids))
patches_layer.CreateFeature(ft)
ft = None
mask.append(tile_id)
fids += 1
if verbose == 1:
progress(coord_grid.shape[0], coord_grid.shape[0],
"Patch generation")
mask = np.array(mask, dtype=int)
if generate_grid_geom is True:
if out_dir is None:
output_geom = patches_ds
else:
raster_basename = metadata["basename"]
geom_name = f"{prefix}{raster_basename}_geom_{str(size)}{postfix}.gpkg"
output_geom = os.path.join(out_dir, geom_name)
overwrite_required(output_geom, overwrite)
remove_if_overwrite(output_geom, overwrite)
if verbose == 1:
print("Writing output geometry..")
internal_vector_to_disk(patches_ds,
output_geom,
overwrite=overwrite)
if verbose == 1:
print("Writing numpy array..")
output_blocks = []
for raster in in_rasters:
base = None
basename = None
output_block = None
if out_dir is not None:
base = os.path.basename(raster)
basename = os.path.splitext(base)[0]
output_block = os.path.join(out_dir +
f"{prefix}{basename}{postfix}.npy")
metadata = internal_raster_to_metadata(raster)
if generate_grid_geom is True or clip_geom is not None:
output_shape = (row_count, size, size, metadata["band_count"])
else:
output_shape = (all_rows, size, size, metadata["band_count"])
input_datatype = metadata["datatype"]
output_array = np.empty(output_shape, dtype=input_datatype)
# if clip_geom is not None:
# ref = raster_to_array(raster, filled=True, extent=tiled_extent)
# else:
ref = raster_to_array(raster, filled=True)
for k, offset in enumerate(in_offsets):
start = 0
if k > 0:
start = offset_rows_cumsum[k - 1]
blocks = None
if (k == 0 and generate_border_patches
and (border_patches_needed_x or border_patches_needed_y)):
blocks = array_to_blocks(
ref,
(size, size),
offset,
border_patches_needed_x,
border_patches_needed_y,
)
else:
blocks = array_to_blocks(ref, (size, size), offset)
output_array[start:offset_rows_cumsum[k]] = blocks
if generate_grid_geom is False and clip_geom is None:
if out_dir is None:
output_blocks.append(output_array)
else:
output_blocks.append(output_block)
np.save(output_block, output_array)
else:
if out_dir is None:
output_blocks.append(output_array[mask])
else:
output_blocks.append(output_block)
np.save(output_block, output_array[mask])
if verify_output and generate_grid_geom:
test_extraction(
in_rasters,
output_blocks,
output_geom,
samples=verification_samples,
grid_layer_index=0,
verbose=verbose,
)
if len(output_blocks) == 1:
output_blocks = output_blocks[0]
return (output_blocks, output_geom)
def test_extraction(
rasters: Union[list, str, gdal.Dataset],
arrays: Union[list, np.ndarray],
grid: Union[ogr.DataSource, str],
samples: int = 1000, # if 0, all
grid_layer_index: int = 0,
verbose: int = 1,
) -> bool:
"""Validates the output of the patch_extractor. Useful if you need peace of mind.
Set samples to 0 to tests everything.
Args:
rasters (list of rasters | path | raster): The raster(s) used.
arrays (list of arrays | ndarray): The arrays generated.
grid (vector | vector_path): The grid generated.
**kwargs:
samples (int): The amount of patches to randomly test. If 0 all patches will be
tested. This is a long process, so consider only testing everything if absolutely
necessary.
grid_layer_index (int): If the grid is part of a multi-layer vector, specify the
index of the grid.
verbose (int): If 1 will output messages on progress.
Returns:
True if the extraction is valid. Raises an error otherwise.
"""
type_check(rasters, [list, str, gdal.Dataset], "rasters")
type_check(arrays, [list, str, np.ndarray], "arrays")
type_check(grid, [list, str, ogr.DataSource], "grid")
type_check(samples, [int], "samples")
type_check(grid_layer_index, [int], "clip_layer_index")
type_check(verbose, [int], "verbose")
in_rasters = to_raster_list(rasters)
in_arrays = arrays
if verbose == 1:
print("Verifying integrity of output grid..")
# grid_memory = open_vector(internal_vector_to_memory(grid))
grid_memory = open_vector(grid)
grid_metadata = internal_vector_to_metadata(grid)
grid_projection = grid_metadata["projection_osr"]
if grid_layer_index > (grid_metadata["layer_count"] - 1):
raise ValueError(
f"Requested non-existing layer index: {grid_layer_index}")
grid_layer = grid_memory.GetLayer(grid_layer_index)
# Select sample fids
feature_count = grid_metadata["layers"][grid_layer_index]["feature_count"]
test_samples = samples if samples > 0 else feature_count
max_test = min(test_samples, feature_count) - 1
test_fids = np.array(random.sample(range(0, feature_count), max_test),
dtype="uint64")
mem_driver = ogr.GetDriverByName("ESRI Shapefile")
for index, raster in enumerate(in_rasters):
test_rast = open_raster(raster)
test_array = in_arrays[index]
if isinstance(test_array, str):
if not os.path.exists(test_array):
raise ValueError(f"Numpy array does not exist: {test_array}")
try:
test_array = np.load(in_arrays[index])
except:
raise Exception(
f"Attempted to read numpy raster from: {in_arrays[index]}")
base = os.path.basename(raster)
basename = os.path.splitext(base)[0]
if verbose == 1:
print(f"Testing: {basename}")
tested = 0
for test in test_fids:
feature = grid_layer.GetFeature(test)
if feature is None:
raise Exception(f"Feature not found: {test}")
test_ds_path = f"/vsimem/test_mem_grid_{uuid4().int}.gpkg"
test_ds = mem_driver.CreateDataSource(test_ds_path)
test_ds_lyr = test_ds.CreateLayer("test_mem_grid_layer",
geom_type=ogr.wkbPolygon,
srs=grid_projection)
test_ds_lyr.CreateFeature(feature.Clone())
test_ds.SyncToDisk()
clipped = internal_clip_raster(
test_rast,
test_ds_path,
adjust_bbox=False,
crop_to_geom=True,
all_touch=False,
)
if clipped is None:
raise Exception(
"Error while clipping raster. Likely a bad extraction.")
ref_image = raster_to_array(clipped, filled=True)
image_block = test_array[test]
if not np.array_equal(ref_image, image_block):
# from matplotlib import pyplot as plt; plt.imshow(ref_image[:,:,0]); plt.show()
raise Exception(
f"Image {basename} and grid cell did not match..")
if verbose == 1:
progress(tested, len(test_fids) - 1, "Verifying..")
tested += 1
return True
def _warp_raster(
raster: Union[str, gdal.Dataset],
out_path: Optional[str] = None,
projection: Optional[Union[int, str, gdal.Dataset, ogr.DataSource,
osr.SpatialReference]] = None,
clip_geom: Optional[Union[str, ogr.DataSource]] = None,
target_size: Optional[Union[Tuple[Number], Number]] = None,
target_in_pixels: bool = False,
resample_alg: str = "nearest",
crop_to_geom: bool = True,
all_touch: bool = True,
adjust_bbox: bool = True,
overwrite: bool = True,
creation_options: Union[list, None] = None,
src_nodata: Union[str, int, float] = "infer",
dst_nodata: Union[str, int, float] = "infer",
layer_to_clip: int = 0,
prefix: str = "",
postfix: str = "_resampled",
) -> str:
"""WARNING: INTERNAL. DO NOT USE."""
raster_list, path_list = ready_io_raster(raster, out_path, overwrite,
prefix, postfix)
origin = open_raster(raster_list[0])
out_name = path_list[0]
raster_metadata = raster_to_metadata(origin, create_geometry=True)
# options
warp_options = []
if all_touch:
warp_options.append("CUTLINE_ALL_TOUCHED=TRUE")
else:
warp_options.append("CUTLINE_ALL_TOUCHED=FALSE")
origin_projection: osr.SpatialReference = raster_metadata["projection_osr"]
origin_extent: ogr.Geometry = raster_metadata["extent_geom_latlng"]
target_projection = origin_projection
if projection is not None:
target_projection = parse_projection(projection)
if clip_geom is not None:
if is_raster(clip_geom):
opened_raster = open_raster(clip_geom)
clip_metadata_raster = raster_to_metadata(opened_raster,
create_geometry=True)
clip_ds = clip_metadata_raster["extent_datasource"]
clip_metadata = internal_vector_to_metadata(clip_ds,
create_geometry=True)
elif is_vector(clip_geom):
clip_ds = open_vector(clip_geom)
clip_metadata = internal_vector_to_metadata(clip_ds,
create_geometry=True)
else:
if file_exists(clip_geom):
raise ValueError(f"Unable to parse clip geometry: {clip_geom}")
else:
raise ValueError(f"Unable to find clip geometry {clip_geom}")
if layer_to_clip > (clip_metadata["layer_count"] - 1):
raise ValueError("Requested an unable layer_to_clip.")
clip_projection = clip_metadata["projection_osr"]
clip_extent = clip_metadata["extent_geom_latlng"]
# Fast check: Does the extent of the two inputs overlap?
if not origin_extent.Intersects(clip_extent):
raise Exception("Clipping geometry did not intersect raster.")
# Check if projections match, otherwise reproject target geom.
if not target_projection.IsSame(clip_projection):
clip_metadata["extent"] = reproject_extent(
clip_metadata["extent"],
clip_projection,
target_projection,
)
# The extent needs to be reprojected to the target.
# this ensures that adjust_bbox works.
x_min_og, y_max_og, x_max_og, y_min_og = reproject_extent(
raster_metadata["extent"],
origin_projection,
target_projection,
)
output_bounds = (x_min_og, y_min_og, x_max_og, y_max_og
) # gdal_warp format
if crop_to_geom:
if adjust_bbox:
output_bounds = align_bbox(
raster_metadata["extent"],
clip_metadata["extent"],
raster_metadata["pixel_width"],
raster_metadata["pixel_height"],
warp_format=True,
)
else:
x_min_og, y_max_og, x_max_og, y_min_og = clip_metadata[
"extent"]
output_bounds = (
x_min_og,
y_min_og,
x_max_og,
y_max_og,
) # gdal_warp format
if clip_metadata["layer_count"] > 1:
clip_ds = vector_to_memory(
clip_ds,
memory_path=f"clip_geom_{uuid4().int}.gpkg",
layer_to_extract=layer_to_clip,
)
elif not isinstance(clip_ds, str):
clip_ds = vector_to_memory(
clip_ds,
memory_path=f"clip_geom_{uuid4().int}.gpkg",
)
if clip_ds is None:
raise ValueError(f"Unable to parse input clip geom: {clip_geom}")
x_res, y_res, x_pixels, y_pixels = raster_size_from_list(
target_size, target_in_pixels)
out_format = path_to_driver_raster(out_name)
out_creation_options = default_options(creation_options)
# nodata
out_nodata = None
if src_nodata is not None:
out_nodata = raster_metadata["nodata_value"]
else:
if dst_nodata == "infer":
out_nodata = gdal_nodata_value_from_type(
raster_metadata["datatype_gdal_raw"])
else:
out_nodata = dst_nodata
# Removes file if it exists and overwrite is True.
remove_if_overwrite(out_path, overwrite)
warped = gdal.Warp(
out_name,
origin,
xRes=x_res,
yRes=y_res,
width=x_pixels,
height=y_pixels,
cutlineDSName=clip_ds,
outputBounds=output_bounds,
format=out_format,
srcSRS=origin_projection,
dstSRS=target_projection,
resampleAlg=translate_resample_method(resample_alg),
creationOptions=out_creation_options,
warpOptions=warp_options,
srcNodata=src_nodata,
dstNodata=out_nodata,
targetAlignedPixels=False,
cropToCutline=False,
multithread=True,
)
if warped is None:
raise Exception(f"Error while warping raster: {raster}")
return out_name
def download_s1_tile(
scihub_username,
scihub_password,
onda_username,
onda_password,
destination,
footprint,
date=("20200601", "20210101"),
orbitdirection="ASCENDING", # ASCENDING, DESCENDING
min_overlap=0.50,
producttype="GRD",
sensoroperationalmode="IW",
polarisationmode="VV VH",
api_url="https://apihub.copernicus.eu/apihub/",
):
api = SentinelAPI(scihub_username, scihub_password, api_url, timeout=60)
if is_vector:
geom = internal_vector_to_metadata(footprint, create_geometry=True)
elif is_raster:
geom = raster_to_metadata(footprint, create_geometry=True)
products = api.query(
geom["extent_wkt_latlng"],
date=date,
platformname="Sentinel-1",
orbitdirection=orbitdirection,
producttype=producttype,
sensoroperationalmode=sensoroperationalmode,
polarisationmode=polarisationmode,
timeout=60,
)
download_products = OrderedDict()
download_ids = []
zero_contents = 0
geom_footprint = ogr.CreateGeometryFromWkt(geom["extent_wkt_latlng"])
for product in products:
dic = products[product]
img_footprint = ogr.CreateGeometryFromWkt(dic["footprint"])
img_area = img_footprint.GetArea()
intersection = img_footprint.Intersection(geom_footprint)
within = img_footprint.Intersection(intersection)
within_area = within.GetArea()
overlap_img = within_area / img_area
overlap_geom = within_area / geom_footprint.GetArea()
if max(overlap_img, overlap_geom) > min_overlap:
download_products[product] = dic
download_ids.append(product)
if len(download_products) > 0:
print(f"Downloading {len(download_products)} files.")
downloaded = []
for img_id in download_ids:
out_path = destination + download_products[img_id][
"filename"] + ".zip"
if os.path.exists(out_path):
print(f"Skipping {out_path}")
continue
download_url = f"https://catalogue.onda-dias.eu/dias-catalogue/Products({img_id})/$value"
try:
content_size = get_content_size(download_url,
out_path,
auth=(onda_username,
onda_password))
except Exception as e:
print(f"Failed to get content size for {img_id}")
print(e)
continue
if content_size == 0:
zero_contents += 1
print(
f"{img_id} requested from Archive but was not downloaded.")
continue
try:
if content_size > 0:
download(
download_url,
out_path,
auth=(onda_username, onda_password),
verbose=True,
)
downloaded.append(img_id)
except Exception as e:
print(f"Error downloading {img_id}: {e}")
return downloaded
else:
print("No images found")
return []
def rasterize_vector(
vector,
pixel_size,
out_path=None,
extent=None,
all_touch=False,
dtype="uint8",
optim="raster",
band=1,
fill_value=0,
nodata_value=None,
check_memory=True,
burn_value=1,
attribute=None,
):
vector_fn = vector
if out_path is None:
raster_fn = f"/vsimem/{str(uuid4())}.tif"
else:
raster_fn = out_path
# Open the data source and read in the extent
source_ds = open_vector(vector_fn)
source_meta = internal_vector_to_metadata(vector_fn)
source_layer = source_ds.GetLayer()
x_min, x_max, y_min, y_max = source_layer.GetExtent()
# Create the destination data source
x_res = int((x_max - x_min) / pixel_size)
y_res = int((y_max - y_min) / pixel_size)
if extent is not None:
extent_vector = internal_vector_to_metadata(extent)
extent_dict = extent_vector["extent_dict"]
x_res = int((extent_dict["right"] - extent_dict["left"]) / pixel_size)
y_res = int((extent_dict["top"] - extent_dict["bottom"]) / pixel_size)
x_min = extent_dict["left"]
y_max = extent_dict["top"]
if check_memory is False:
gdal.SetConfigOption("CHECK_DISK_FREE_SPACE", "FALSE")
try:
target_ds = gdal.GetDriverByName("GTiff").Create(
raster_fn,
x_res,
y_res,
1,
numpy_to_gdal_datatype2(dtype),
)
finally:
gdal.SetConfigOption("CHECK_DISK_FREE_SPACE", "TRUE")
if target_ds is None:
raise Exception("Unable to rasterize.")
target_ds.SetGeoTransform((x_min, pixel_size, 0, y_max, 0, -pixel_size))
target_ds.SetProjection(source_meta["projection"])
band = target_ds.GetRasterBand(1)
if nodata_value is not None:
band.SetNoDataValue(nodata_value)
else:
band.Fill(fill_value)
options = []
if all_touch == True:
options.append("ALL_TOUCHED=TRUE")
else:
options.append("ALL_TOUCHED=FALSE")
if optim == "raster":
options.append("OPTIM=RASTER")
elif optim == "vector":
options.append("OPTIM=VECTOR")
else:
options.append("OPTIM=AUTO")
if attribute is None:
gdal.RasterizeLayer(
target_ds,
[1],
source_layer,
burn_values=[burn_value],
options=options,
)
else:
options.append(f"ATTRIBUTE={attribute}")
gdal.RasterizeLayer(target_ds, [1],
source_layer,
options=default_options(options))
return raster_fn
def internal_intersect_vector(
vector: Union[str, ogr.DataSource],
clip_geom: Union[str, ogr.DataSource, gdal.Dataset],
out_path: Optional[str] = None,
to_extent: bool = False,
process_layer: int = 0,
process_layer_clip: int = 0,
add_index: bool = True,
preserve_fid: bool = True,
overwrite: bool = True,
return_bool: bool = False,
) -> str:
"""Clips a vector to a geometry.
Returns:
A clipped ogr.Datasource or the path to one.
"""
type_check(vector, [ogr.DataSource, str, list], "vector")
type_check(clip_geom, [ogr.DataSource, gdal.Dataset, str, list, tuple], "clip_geom")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(to_extent, [bool], "to_extent")
type_check(process_layer, [int], "process_layer")
type_check(process_layer_clip, [int], "process_layer_clip")
type_check(add_index, [bool], "add_index")
type_check(preserve_fid, [bool], "preserve_fid")
type_check(overwrite, [bool], "overwrite")
_vector_list, path_list = ready_io_vector(vector, out_path, overwrite=overwrite)
out_name = path_list[0]
match_projection = internal_reproject_vector(clip_geom, vector)
geometry_to_clip = open_vector(match_projection)
merged = open_vector(merge_vectors([vector, match_projection]))
if add_index:
vector_add_index(merged)
vector_metadata = internal_vector_to_metadata(vector)
vector_layername = vector_metadata["layers"][process_layer]["layer_name"]
vector_geom_col = vector_metadata["layers"][process_layer]["column_geom"]
clip_geom_metadata = internal_vector_to_metadata(geometry_to_clip)
clip_geom_layername = clip_geom_metadata["layers"][process_layer_clip]["layer_name"]
clip_geom_col = clip_geom_metadata["layers"][process_layer_clip]["column_geom"]
if return_bool:
sql = f"SELECT A.* FROM '{vector_layername}' A, '{clip_geom_layername}' B WHERE ST_INTERSECTS(A.{vector_geom_col}, B.{clip_geom_col});"
else:
sql = f"SELECT A.* FROM '{vector_layername}' A, '{clip_geom_layername}' B WHERE ST_INTERSECTS(A.{vector_geom_col}, B.{clip_geom_col});"
result = merged.ExecuteSQL(sql, dialect="SQLITE")
if return_bool:
if result.GetFeatureCount() == 0:
return False
else:
return True
driver = ogr.GetDriverByName(path_to_driver_vector(out_name))
destination: ogr.DataSource = driver.CreateDataSource(out_name)
destination.CopyLayer(result, vector_layername, ["OVERWRITE=YES"])
if destination is None:
raise Exception("Error while running intersect.")
destination.FlushCache()
return out_name
def align_rasters(
rasters: List[Union[str, gdal.Dataset]],
out_path: Optional[Union[List[str], str]] = None,
master: Optional[Union[gdal.Dataset, str]] = None,
postfix: str = "_aligned",
bounding_box: Union[str, gdal.Dataset, ogr.DataSource, list,
tuple] = "intersection",
resample_alg: str = "nearest",
target_size: Optional[Union[tuple, list, int, float, str,
gdal.Dataset]] = None,
target_in_pixels: bool = False,
projection: Optional[Union[int, str, gdal.Dataset, ogr.DataSource,
osr.SpatialReference]] = None,
overwrite: bool = True,
creation_options: list = [],
src_nodata: Optional[Union[str, int, float]] = "infer",
dst_nodata: Optional[Union[str, int, float]] = "infer",
prefix: str = "",
ram=8000,
skip_existing=False,
) -> List[str]:
type_check(rasters, [list], "rasters")
type_check(out_path, [list, str], "out_path", allow_none=True)
type_check(master, [list, str], "master", allow_none=True)
type_check(bounding_box, [str, gdal.Dataset, ogr.DataSource, list, tuple],
"bounding_box")
type_check(resample_alg, [str], "resample_alg")
type_check(
target_size,
[tuple, list, int, float, str, gdal.Dataset],
"target_size",
allow_none=True,
)
type_check(
target_in_pixels,
[int, str, gdal.Dataset, ogr.DataSource, osr.SpatialReference],
"target_in_pixels",
allow_none=True,
)
type_check(overwrite, [bool], "overwrite")
type_check(creation_options, [list], "creation_options")
type_check(src_nodata, [str, int, float], "src_nodata", allow_none=True)
type_check(dst_nodata, [str, int, float], "dst_nodata", allow_none=True)
type_check(prefix, [str], "prefix")
type_check(postfix, [str], "postfix")
raster_list, path_list = ready_io_raster(
rasters,
out_path,
overwrite=overwrite,
prefix=prefix,
postfix=postfix,
uuid=False,
)
x_pixels = None
y_pixels = None
x_res = None
y_res = None
target_projection = None
target_bounds = None
reprojected_rasters: List[str] = []
# Read the metadata for each raster.
# Catalogue the used projections, to choose the most common one if necessary.
used_projections: List[dict] = []
metadata: List[str] = []
for raster in rasters:
meta = raster_to_metadata(raster)
metadata.append(meta)
used_projections.append(meta["projection"])
# If there is a master layer, copy information from that layer.
if master is not None:
master_metadata = raster_to_metadata(master)
target_projection = master_metadata["projection_osr"]
x_min, y_max, x_max, y_min = master_metadata["extent"]
# Set the target values.
target_bounds = (x_min, y_min, x_max, y_max)
x_res = master_metadata["pixel_width"]
y_res = master_metadata["pixel_height"]
x_pixels = master_metadata["width"]
y_pixels = master_metadata["height"]
target_size = (x_res, y_res)
target_in_pixels = False
# We allow overwrite of parameters specifically set.
# Handle projection
if projection is not None:
target_projection = parse_projection(projection)
# If no projection is specified, other from master or parameters. The most common one is chosen.
elif target_projection is None:
# Sort and count the projections
projection_counter: dict = {}
for proj in used_projections:
if proj in projection_counter:
projection_counter[proj] += 1
else:
projection_counter[proj] = 1
# Choose most common projection
most_common_projection = sorted(projection_counter,
key=projection_counter.get,
reverse=True)
target_projection = parse_projection(most_common_projection[0])
if target_size is not None:
# If a raster is input, use it's pixel size as target values.
if isinstance(target_size, (gdal.Dataset, str)):
if isinstance(target_size, str) and not is_raster(target_size):
raise ValueError(
f"Unable to parse the raster used for target_size: {target_size}"
)
# Reprojection is necessary to ensure the correct pixel_size
reprojected_target_size = internal_reproject_raster(
target_size, target_projection)
target_size_raster = raster_to_metadata(reprojected_target_size)
# Set the target values.
x_res = target_size_raster["width"]
y_res = target_size_raster["height"]
else:
# If a list, tuple, int or float is passed. Turn them into target values.
x_res, y_res, x_pixels, y_pixels = raster_size_from_list(
target_size, target_in_pixels)
# If nothing has been specified, we will infer the pixel_size based on the median of all input rasters.
elif x_res is None and y_res is None and x_pixels is None and y_pixels is None:
# Ready numpy arrays for insertion
x_res_arr = np.empty(len(raster_list), dtype="float32")
y_res_arr = np.empty(len(raster_list), dtype="float32")
for index, raster in enumerate(raster_list):
# It is necessary to reproject each raster, as pixel height and width might be different after projection.
reprojected = internal_reproject_raster(raster, target_projection)
target_size_raster = raster_to_metadata(reprojected)
# Add the pixel sizes to the numpy arrays
x_res_arr[index] = target_size_raster["pixel_width"]
y_res_arr[index] = target_size_raster["pixel_height"]
# Keep track of the reprojected arrays so we only reproject rasters once.
reprojected_rasters.append(reprojected)
# Use the median values of pixel sizes as target values.
x_res = np.median(x_res_arr)
y_res = np.median(y_res_arr)
if target_bounds is None:
# If a bounding box is supplied, simply use that one. It must be in the target projection.
if isinstance(bounding_box, (list, tuple)):
if len(bounding_box) != 4:
raise ValueError(
"bounding_box as a list/tuple must have 4 values.")
target_bounds = bounding_box
# If the bounding box is a raster. Take the extent and reproject it to the target projection.
elif is_raster(bounding_box):
reprojected_bbox_raster = raster_to_metadata(
internal_reproject_raster(bounding_box, target_projection))
x_min, y_max, x_max, y_min = reprojected_bbox_raster["extent"]
# add to target values.
target_bounds = (x_min, y_min, x_max, y_max)
# If the bounding box is a raster. Take the extent and reproject it to the target projection.
elif is_vector(bounding_box):
reprojected_bbox_vector = internal_vector_to_metadata(
internal_reproject_vector(bounding_box, target_projection))
x_min, y_max, x_max, y_min = reprojected_bbox_vector["extent"]
# add to target values.
target_bounds = (x_min, y_min, x_max, y_max)
# If the bounding box is a string, we either take the union or the intersection of all the
# bounding boxes of the input rasters.
elif isinstance(bounding_box, str):
if bounding_box == "intersection" or bounding_box == "union":
extents = []
# If the rasters have not been reprojected, reproject them now.
if len(reprojected_rasters) != len(raster_list):
reprojected_rasters = []
for raster in raster_list:
raster_metadata = raster_to_metadata(raster)
if raster_metadata["projection_osr"].IsSame(
target_projection):
reprojected_rasters.append(raster)
else:
reprojected = internal_reproject_raster(
raster, target_projection)
reprojected_rasters.append(reprojected)
# Add the extents of the reprojected rasters to the extents list.
for reprojected_raster in reprojected_rasters:
reprojected_raster_metadata = dict(
raster_to_metadata(reprojected_raster))
extents.append(reprojected_raster_metadata["extent"])
# Placeholder values
x_min, y_max, x_max, y_min = extents[0]
# Loop the extents. Narrowing if intersection, expanding if union.
for index, extent in enumerate(extents):
if index == 0:
continue
if bounding_box == "intersection":
if extent[0] > x_min:
x_min = extent[0]
if extent[1] < y_max:
y_max = extent[1]
if extent[2] < x_max:
x_max = extent[2]
if extent[3] > y_min:
y_min = extent[3]
elif bounding_box == "union":
if extent[0] < x_min:
x_min = extent[0]
if extent[1] > y_max:
y_max = extent[1]
if extent[2] > x_max:
x_max = extent[2]
if extent[3] < y_min:
y_min = extent[3]
# Add to target values.
target_bounds = (x_min, y_min, x_max, y_max)
else:
raise ValueError(
f"Unable to parse or infer target_bounds: {target_bounds}")
else:
raise ValueError(
f"Unable to parse or infer target_bounds: {target_bounds}")
"""
If the rasters have not been reprojected, we reproject them now.
The reprojection is necessary as warp has to be a two step process
in order to align the rasters properly. This might not be necessary
in a future version of gdal.
"""
if len(reprojected_rasters) != len(raster_list):
reprojected_rasters = []
for raster in raster_list:
raster_metadata = raster_to_metadata(raster)
# If the raster is already the correct projection, simply append the raster.
if raster_metadata["projection_osr"].IsSame(target_projection):
reprojected_rasters.append(raster)
else:
reprojected = internal_reproject_raster(
raster, target_projection)
reprojected_rasters.append(reprojected)
# If any of the target values are still undefined. Throw an error!
if target_projection is None or target_bounds is None:
raise Exception(
"Error while preparing the target projection or bounds.")
if x_res is None and y_res is None and x_pixels is None and y_pixels is None:
raise Exception("Error while preparing the target pixel size.")
# This is the list of rasters to return. If output is not memory, it's a list of paths.
return_list: List[str] = []
for index, raster in enumerate(reprojected_rasters):
raster_metadata = raster_to_metadata(raster)
out_name = path_list[index]
out_format = path_to_driver_raster(out_name)
if skip_existing and os.path.exists(out_name):
return_list.append(out_name)
continue
# Handle nodata.
out_src_nodata = None
out_dst_nodata = None
if src_nodata == "infer":
out_src_nodata = raster_metadata["nodata_value"]
if out_src_nodata is None:
out_src_nodata = gdal_nodata_value_from_type(
raster_metadata["datatype_gdal_raw"])
elif src_nodata == None:
out_src_nodata = None
elif not isinstance(src_nodata, str):
out_src_nodata = src_nodata
if dst_nodata == "infer":
out_dst_nodata = out_src_nodata
elif dst_nodata == False or dst_nodata == None:
out_dst_nodata = None
elif src_nodata == None:
out_dst_nodata = None
elif not isinstance(dst_nodata, str):
out_dst_nodata = dst_nodata
# Removes file if it exists and overwrite is True.
remove_if_overwrite(out_name, overwrite)
# Hand over to gdal.Warp to do the heavy lifting!
warped = gdal.Warp(
out_name,
raster,
xRes=x_res,
yRes=y_res,
width=x_pixels,
height=y_pixels,
dstSRS=target_projection,
outputBounds=target_bounds,
format=out_format,
resampleAlg=translate_resample_method(resample_alg),
creationOptions=default_options(creation_options),
srcNodata=out_src_nodata,
dstNodata=out_dst_nodata,
targetAlignedPixels=False,
cropToCutline=False,
multithread=True,
warpMemoryLimit=ram,
)
if warped == None:
raise Exception("Error while warping rasters.")
return_list.append(out_name)
if not rasters_are_aligned(return_list, same_extent=True):
raise Exception("Error while aligning rasters. Output is not aligned")
return return_list
def internal_multipart_to_singlepart(
vector: Union[str, ogr.DataSource],
out_path: Optional[str] = None,
copy_attributes: bool = False,
overwrite: bool = True,
add_index: bool = True,
process_layer: int = -1,
verbose: int = 1,
) -> str:
type_check(vector, [str, ogr.DataSource], "vector")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(overwrite, [bool], "overwrite")
type_check(add_index, [bool], "add_index")
type_check(process_layer, [int], "process_layer")
type_check(verbose, [int], "verbose")
vector_list, path_list = ready_io_vector(vector, out_path, overwrite=overwrite)
ref = open_vector(vector_list[0])
out_name = path_list[0]
driver = ogr.GetDriverByName(path_to_driver_vector(out_name))
metadata = internal_vector_to_metadata(ref)
remove_if_overwrite(out_name, overwrite)
destination = driver.CreateDataSource(out_name)
for index, layer_meta in enumerate(metadata["layers"]):
if process_layer != -1 and index != process_layer:
continue
if verbose == 1:
layer_name = layer_meta["layer_name"]
print(f"Splitting layer: {layer_name}")
target_unknown = False
if layer_meta["geom_type_ogr"] == 4: # MultiPoint
target_type = 1 # Point
elif layer_meta["geom_type_ogr"] == 5: # MultiLineString
target_type = 2 # LineString
elif layer_meta["geom_type_ogr"] == 6: # MultiPolygon
target_type = 3 # Polygon
elif layer_meta["geom_type_ogr"] == 1004: # MultiPoint (z)
target_type = 1001 # Point (z)
elif layer_meta["geom_type_ogr"] == 1005: # MultiLineString (z)
target_type = 1002 # LineString (z)
elif layer_meta["geom_type_ogr"] == 1006: # MultiPolygon (z)
target_type = 1003 # Polygon (z)
elif layer_meta["geom_type_ogr"] == 2004: # MultiPoint (m)
target_type = 2001 # Point (m)
elif layer_meta["geom_type_ogr"] == 2005: # MultiLineString (m)
target_type = 2002 # LineString (m)
elif layer_meta["geom_type_ogr"] == 2006: # MultiPolygon (m)
target_type = 2003 # Polygon (m)
elif layer_meta["geom_type_ogr"] == 3004: # MultiPoint (zm)
target_type = 3001 # Point (m)
elif layer_meta["geom_type_ogr"] == 3005: # MultiLineString (zm)
target_type = 3002 # LineString (m)
elif layer_meta["geom_type_ogr"] == 3006: # MultiPolygon (zm)
target_type = 3003 # Polygon (m)
else:
target_unknown = True
target_type = layer_meta["geom_type_ogr"]
destination_layer = destination.CreateLayer(
layer_meta["layer_name"], layer_meta["projection_osr"], target_type
)
layer_defn = destination_layer.GetLayerDefn()
field_count = layer_meta["field_count"]
original_target = ref.GetLayerByIndex(index)
feature_count = original_target.GetFeatureCount()
if copy_attributes:
first_feature = original_target.GetNextFeature()
original_target.ResetReading()
if verbose == 1:
print("Creating attribute fields")
for field_id in range(field_count):
field_defn = first_feature.GetFieldDefnRef(field_id)
fname = field_defn.GetName()
ftype = field_defn.GetTypeName()
fwidth = field_defn.GetWidth()
fprecision = field_defn.GetPrecision()
if ftype == "String" or ftype == "Date":
fielddefn = ogr.FieldDefn(fname, ogr.OFTString)
fielddefn.SetWidth(fwidth)
elif ftype == "Real":
fielddefn = ogr.FieldDefn(fname, ogr.OFTReal)
fielddefn.SetWidth(fwidth)
fielddefn.SetPrecision(fprecision)
else:
fielddefn = ogr.FieldDefn(fname, ogr.OFTInteger)
destination_layer.CreateField(fielddefn)
for _ in range(feature_count):
feature = original_target.GetNextFeature()
geom = feature.GetGeometryRef()
if target_unknown:
out_feat = ogr.Feature(layer_defn)
out_feat.SetGeometry(geom)
if copy_attributes:
for field_id in range(field_count):
values = feature.GetField(field_id)
out_feat.SetField(field_id, values)
destination_layer.CreateFeature(out_feat)
for geom_part in geom:
out_feat = ogr.Feature(layer_defn)
out_feat.SetGeometry(geom_part)
if copy_attributes:
for field_id in range(field_count):
values = feature.GetField(field_id)
out_feat.SetField(field_id, values)
destination_layer.CreateFeature(out_feat)
if verbose == 1:
progress(_, feature_count - 1, "Splitting.")
if add_index:
vector_add_index(destination)
return out_name
def _clip_raster(
raster: Union[str, gdal.Dataset],
clip_geom: Union[str, ogr.DataSource, gdal.Dataset],
out_path: Optional[str] = None,
resample_alg: str = "nearest",
crop_to_geom: bool = True,
adjust_bbox: bool = True,
all_touch: bool = True,
overwrite: bool = True,
creation_options: list = [],
dst_nodata: Union[str, int, float] = "infer",
layer_to_clip: int = 0,
prefix: str = "",
postfix: str = "_clipped",
verbose: int = 1,
uuid: bool = False,
ram: int = 8000,
) -> str:
"""OBS: Internal. Single output.
Clips a raster(s) using a vector geometry or the extents of
a raster.
"""
type_check(raster, [str, gdal.Dataset], "raster")
type_check(clip_geom, [str, ogr.DataSource, gdal.Dataset], "clip_geom")
type_check(out_path, [str], "out_path", allow_none=True)
type_check(resample_alg, [str], "resample_alg")
type_check(crop_to_geom, [bool], "crop_to_geom")
type_check(adjust_bbox, [bool], "adjust_bbox")
type_check(all_touch, [bool], "all_touch")
type_check(dst_nodata, [str, int, float], "dst_nodata")
type_check(layer_to_clip, [int], "layer_to_clip")
type_check(overwrite, [bool], "overwrite")
type_check(creation_options, [list], "creation_options")
type_check(prefix, [str], "prefix")
type_check(postfix, [str], "postfix")
type_check(verbose, [int], "verbose")
type_check(uuid, [bool], "uuid")
_, path_list = ready_io_raster(raster,
out_path,
overwrite=overwrite,
prefix=prefix,
postfix=postfix,
uuid=uuid)
if out_path is not None:
if "vsimem" not in out_path:
if not os.path.isdir(os.path.split(os.path.normpath(out_path))[0]):
raise ValueError(
f"out_path folder does not exists: {out_path}")
# Input is a vector.
if is_vector(clip_geom):
clip_ds = open_vector(clip_geom)
clip_metadata = internal_vector_to_metadata(
clip_ds, process_layer=layer_to_clip)
if clip_metadata["layer_count"] > 1:
clip_ds = internal_vector_to_memory(clip_ds,
layer_to_extract=layer_to_clip)
if isinstance(clip_ds, ogr.DataSource):
clip_ds = clip_ds.GetName()
# Input is a raster (use extent)
elif is_raster(clip_geom):
clip_metadata = raster_to_metadata(clip_geom, create_geometry=True)
clip_metadata["layer_count"] = 1
clip_ds = clip_metadata["extent_datasource"].GetName()
else:
if file_exists(clip_geom):
raise ValueError(f"Unable to parse clip geometry: {clip_geom}")
else:
raise ValueError(f"Unable to locate clip geometry {clip_geom}")
if layer_to_clip > (clip_metadata["layer_count"] - 1):
raise ValueError("Requested an unable layer_to_clip.")
if clip_ds is None:
raise ValueError(f"Unable to parse input clip geom: {clip_geom}")
clip_projection = clip_metadata["projection_osr"]
clip_extent = clip_metadata["extent"]
# options
warp_options = []
if all_touch:
warp_options.append("CUTLINE_ALL_TOUCHED=TRUE")
else:
warp_options.append("CUTLINE_ALL_TOUCHED=FALSE")
origin_layer = open_raster(raster)
raster_metadata = raster_to_metadata(raster)
origin_projection = raster_metadata["projection_osr"]
origin_extent = raster_metadata["extent"]
# Check if projections match, otherwise reproject target geom.
if not origin_projection.IsSame(clip_projection):
clip_metadata["extent"] = reproject_extent(
clip_metadata["extent"],
clip_projection,
origin_projection,
)
# Fast check: Does the extent of the two inputs overlap?
if not gdal_bbox_intersects(origin_extent, clip_extent):
raise Exception("Geometries did not intersect.")
output_bounds = raster_metadata["extent_gdal_warp"]
if crop_to_geom:
if adjust_bbox:
output_bounds = align_bbox(
raster_metadata["extent"],
clip_metadata["extent"],
raster_metadata["pixel_width"],
raster_metadata["pixel_height"],
warp_format=True,
)
else:
output_bounds = clip_metadata["extent_gdal_warp"]
# formats
out_name = path_list[0]
out_format = path_to_driver_raster(out_name)
out_creation_options = default_options(creation_options)
# nodata
src_nodata = raster_metadata["nodata_value"]
out_nodata = None
if src_nodata is not None:
out_nodata = src_nodata
else:
if dst_nodata == "infer":
out_nodata = gdal_nodata_value_from_type(
raster_metadata["datatype_gdal_raw"])
elif dst_nodata is None:
out_nodata = None
elif isinstance(dst_nodata, (int, float)):
out_nodata = dst_nodata
else:
raise ValueError(f"Unable to parse nodata_value: {dst_nodata}")
# Removes file if it exists and overwrite is True.
remove_if_overwrite(out_path, overwrite)
if verbose == 0:
gdal.PushErrorHandler("CPLQuietErrorHandler")
clipped = gdal.Warp(
out_name,
origin_layer,
format=out_format,
resampleAlg=translate_resample_method(resample_alg),
targetAlignedPixels=False,
outputBounds=output_bounds,
xRes=raster_metadata["pixel_width"],
yRes=raster_metadata["pixel_height"],
cutlineDSName=clip_ds,
cropToCutline=
False, # GDAL does this incorrectly when targetAlignedPixels is True.
creationOptions=out_creation_options,
warpMemoryLimit=ram,
warpOptions=warp_options,
srcNodata=raster_metadata["nodata_value"],
dstNodata=out_nodata,
multithread=True,
)
if verbose == 0:
gdal.PopErrorHandler()
if clipped is None:
raise Exception("Error while clipping raster.")
return out_name
def internal_reproject_vector(
vector: Union[str, ogr.DataSource],
projection: Union[str, int, ogr.DataSource, gdal.Dataset,
osr.SpatialReference],
out_path: Optional[str] = None,
copy_if_same: bool = False,
overwrite: bool = True,
) -> str:
type_check(vector, [str, ogr.DataSource], "vector")
type_check(
projection,
[str, int, ogr.DataSource, gdal.Dataset, osr.SpatialReference],
"projection",
)
type_check(out_path, [str], "out_path", allow_none=True)
type_check(copy_if_same, [bool], "copy_if_same")
type_check(overwrite, [bool], "overwrite")
vector_list, path_list = ready_io_vector(vector,
out_path,
overwrite=overwrite)
origin = open_vector(vector_list[0])
metadata = internal_vector_to_metadata(origin)
out_name = path_list[0]
origin_projection = metadata["projection_osr"]
target_projection = parse_projection(projection)
if not isinstance(target_projection, osr.SpatialReference):
raise Exception("Error ")
if origin_projection.IsSame(target_projection):
if copy_if_same:
if out_path is None:
return internal_vector_to_memory(origin)
return internal_vector_to_disk(origin, out_name)
else:
return get_vector_path(vector)
# GDAL 3 changes axis order: https://github.com/OSGeo/gdal/issues/1546
if int(osgeo.__version__[0]) >= 3:
origin_projection.SetAxisMappingStrategy(
osr.OAMS_TRADITIONAL_GIS_ORDER)
target_projection.SetAxisMappingStrategy(
osr.OAMS_TRADITIONAL_GIS_ORDER)
coord_trans = osr.CoordinateTransformation(origin_projection,
target_projection)
remove_if_overwrite(out_path, overwrite)
driver = ogr.GetDriverByName(path_to_driver_vector(out_name))
destination: ogr.DataSource = driver.CreateDataSource(out_name)
for layer_idx in range(len(metadata["layers"])):
origin_layer = origin.GetLayerByIndex(layer_idx)
origin_layer_defn = origin_layer.GetLayerDefn()
layer_dict = metadata["layers"][layer_idx]
layer_name = layer_dict["layer_name"]
layer_geom_type = layer_dict["geom_type_ogr"]
destination_layer = destination.CreateLayer(layer_name,
target_projection,
layer_geom_type)
destination_layer_defn = destination_layer.GetLayerDefn()
# Copy field definitions
origin_layer_defn = origin_layer.GetLayerDefn()
for i in range(0, origin_layer_defn.GetFieldCount()):
field_defn = origin_layer_defn.GetFieldDefn(i)
destination_layer.CreateField(field_defn)
# Loop through the input features
for _ in range(origin_layer.GetFeatureCount()):
feature = origin_layer.GetNextFeature()
geom = feature.GetGeometryRef()
geom.Transform(coord_trans)
new_feature = ogr.Feature(destination_layer_defn)
new_feature.SetGeometry(geom)
# Copy field values
for i in range(0, destination_layer_defn.GetFieldCount()):
new_feature.SetField(
destination_layer_defn.GetFieldDefn(i).GetNameRef(),
feature.GetField(i),
)
destination_layer.CreateFeature(new_feature)
destination_layer.ResetReading()
destination_layer = None
destination.FlushCache()
return out_name
def internal_dissolve_vector(
vector: Union[str, ogr.DataSource],
attribute: Optional[str] = None,
out_path: str = None,
overwrite: bool = True,
add_index: bool = True,
process_layer: int = -1,
) -> str:
"""Clips a vector to a geometry."""
type_check(vector, [str, ogr.DataSource], "vector")
type_check(attribute, [str], "attribute", allow_none=True)
type_check(out_path, [str], "out_path", allow_none=True)
type_check(overwrite, [bool], "overwrite")
type_check(add_index, [bool], "add_index")
type_check(process_layer, [int], "process_layer")
vector_list, path_list = ready_io_vector(vector, out_path)
out_name = path_list[0]
out_format = path_to_driver_vector(out_name)
driver = ogr.GetDriverByName(out_format)
ref = open_vector(vector_list[0])
metadata = internal_vector_to_metadata(ref)
Layer_info = TypedDict(
"Layer_info",
{
"name": str,
"geom": str,
"fields": List[str]
},
)
layers: List[Layer_info] = []
if process_layer == -1:
for index in range(len(metadata["layers"])):
layers.append({
"name": metadata["layers"][index]["layer_name"],
"geom": metadata["layers"][index]["column_geom"],
"fields": metadata["layers"][index]["field_names"],
})
else:
layers.append({
"name":
metadata["layers"][process_layer]["layer_name"],
"geom":
metadata["layers"][process_layer]["column_geom"],
"fields":
metadata["layers"][process_layer]["field_names"],
})
destination: ogr.DataSource = driver.CreateDataSource(out_name)
# Check if attribute table is valid
for index in range(len(metadata["layers"])):
layer = layers[index]
if attribute is not None and attribute not in layer["fields"]:
layer_fields = layer["fields"]
raise ValueError(
f"Invalid attribute for layer. Layers has the following fields: {layer_fields}"
)
geom_col = layer["geom"]
name = layer["name"]
sql = None
if attribute is None:
sql = f"SELECT ST_Union({geom_col}) AS geom FROM {name};"
else:
sql = f"SELECT {attribute}, ST_Union({geom_col}) AS geom FROM {name} GROUP BY {attribute};"
result = ref.ExecuteSQL(sql, dialect="SQLITE")
destination.CopyLayer(result, name, ["OVERWRITE=YES"])
if add_index:
vector_add_index(destination)
destination.FlushCache()
return out_name
def download_s2_tile(
scihub_username,
scihub_password,
onda_username,
onda_password,
destination,
aoi_vector,
date_start="20200601",
date_end="20210101",
clouds=10,
producttype="S2MSI2A",
tile=None,
retry_count=10,
retry_wait_min=30,
retry_current=0,
retry_downloaded=[],
api_url="http://apihub.copernicus.eu/apihub",
):
print("Downloading Sentinel-2 tiles")
try:
api = SentinelAPI(scihub_username,
scihub_password,
api_url,
timeout=60)
except Exception as e:
print(e)
raise Exception("Error connecting to SciHub")
if is_vector(aoi_vector):
geom = internal_vector_to_metadata(aoi_vector, create_geometry=True)
elif is_raster(aoi_vector):
geom = raster_to_metadata(aoi_vector, create_geometry=True)
geom_extent = geom["extent_wkt_latlng"]
download_products = OrderedDict()
download_ids = []
date = (date_start, date_end)
if tile is not None and tile != "":
kw = {"raw": f"tileid:{tile} OR filename:*_T{tile}_*"}
try:
products = api.query(
date=date,
platformname="Sentinel-2",
cloudcoverpercentage=(0, clouds),
producttype="S2MSI2A",
timeout=60,
**kw,
)
except Exception as e:
print(e)
raise Exception("Error connecting to SciHub")
else:
try:
products = api.query(
geom_extent,
date=date,
platformname="Sentinel-2",
cloudcoverpercentage=(0, clouds),
producttype=producttype,
)
except Exception as e:
print(e)
raise Exception("Error connecting to SciHub")
for product in products:
dic = products[product]
product_tile = dic["title"].split("_")[-2][1:]
if (tile is not None and tile != "") and product_tile != tile:
continue
download_products[product] = dic
download_ids.append(product)
print(f"Downloading {len(download_products)} tiles")
downloaded = [] + retry_downloaded
for img_id in download_ids:
out_path = destination + download_products[img_id]["filename"] + ".zip"
if out_path in downloaded:
continue
# /footprint url for.
download_url = (
f"https://catalogue.onda-dias.eu/dias-catalogue/Products({img_id})/$value"
)
try:
content_size = get_content_size(download_url,
auth=(onda_username,
onda_password))
except Exception as e:
print(f"Failed to get content size for {img_id}")
print(e)
continue
try:
if content_size > 0:
if os.path.isfile(
out_path) and content_size == os.path.getsize(
out_path):
downloaded.append(out_path)
print(f"Skipping {img_id}")
else:
print(f"Downloading: {img_id}")
download(
download_url,
out_path,
auth=HTTPBasicAuth(onda_username, onda_password),
verbose=False,
skip_if_exists=True,
)
downloaded.append(out_path)
else:
print("Requesting from archive. Not downloaded.")
order_url = f"https://catalogue.onda-dias.eu/dias-catalogue/Products({img_id})/Ens.Order"
order_response = order(order_url,
auth=(onda_username, onda_password))
except Exception as e:
print(f"Error downloading {img_id}: {e}")
if len(downloaded) >= len(download_ids):
return downloaded
elif retry_current < retry_count:
print(
f"Retrying {retry_current}/{retry_count}. Sleeping for {retry_wait_min} minutes."
)
sleep(retry_wait_min * 60)
download_s2_tile(
scihub_username,
scihub_password,
onda_username,
onda_password,
destination,
aoi_vector,
date_start=date_start,
date_end=date_end,
clouds=clouds,
producttype=producttype,
tile=tile,
retry_count=retry_count,
retry_wait_min=retry_wait_min,
retry_current=retry_current + 1,
retry_downloaded=retry_downloaded + downloaded,
)
else:
return retry_downloaded + downloaded
def zonal_statistics(
in_vector,
output_vector=None,
in_rasters=[],
prefixes=[],
stats=["mean", "med", "std"],
):
if len(prefixes) != 0:
if len(in_rasters) != len(prefixes):
raise ValueError("Unable to parse prefixes.")
if isinstance(in_rasters, list):
if len(in_rasters) == 0:
raise ValueError("List of rasters (in_rasters) is empty.")
if len(stats) == 0:
raise ValueError("Unable to parse statistics (stats).")
# Translate stats to integers
stats_translated = stats_to_ints(stats)
# Read the raster meta:
raster_metadata = internal_raster_to_metadata(in_rasters[0])
vector = None
if output_vector is None:
vector = open_vector(in_vector, writeable=True)
else:
vector = internal_vector_to_memory(in_vector)
vector_metadata = internal_vector_to_metadata(vector)
vector_layer = vector.GetLayer()
# Check that projections match
if not vector_metadata["projection_osr"].IsSame(
raster_metadata["projection_osr"]):
if output_vector is None:
vector = internal_reproject_vector(in_vector, in_rasters[0])
else:
vector_path = internal_reproject_vector(in_vector, in_rasters[0],
output_vector)
vector = open_vector(vector_path, writeable=True)
vector_metadata = internal_vector_to_metadata(vector)
vector_layer = vector.GetLayer()
vector_projection = vector_metadata["projection_osr"]
raster_projection = raster_metadata["projection"]
# Read raster data in overlap
raster_transform = np.array(raster_metadata["transform"], dtype=np.float32)
raster_size = np.array(raster_metadata["size"], dtype=np.int32)
raster_extent = get_extent(raster_transform, raster_size)
vector_extent = np.array(vector_layer.GetExtent(), dtype=np.float32)
overlap_extent = get_intersection(raster_extent, vector_extent)
if overlap_extent is False:
print("raster_extent: ", raster_extent)
print("vector_extent: ", vector_extent)
raise Exception("Vector and raster do not overlap!")
(
overlap_aligned_extent,
overlap_aligned_rasterized_size,
overlap_aligned_offset,
) = align_extent(raster_transform, overlap_extent, raster_size)
overlap_transform = np.array(
[
overlap_aligned_extent[0],
raster_transform[1],
0,
overlap_aligned_extent[3],
0,
raster_transform[5],
],
dtype=np.float32,
)
overlap_size = overlap_size_calc(overlap_aligned_extent, raster_transform)
# Loop the features
vector_driver = ogr.GetDriverByName("Memory")
vector_feature_count = vector_layer.GetFeatureCount()
vector_layer.StartTransaction()
# Create fields
vector_layer_defn = vector_layer.GetLayerDefn()
vector_field_counts = vector_layer_defn.GetFieldCount()
vector_current_fields = []
# Get current fields
for i in range(vector_field_counts):
vector_current_fields.append(
vector_layer_defn.GetFieldDefn(i).GetName())
# Add fields where missing
for stat in stats:
for i in range(len(in_rasters)):
field_name = f"{prefixes[i]}{stat}"
if field_name not in vector_current_fields:
field_defn = ogr.FieldDefn(field_name, ogr.OFTReal)
vector_layer.CreateField(field_defn)
rasterized_features = []
sizes = np.zeros((vector_feature_count, 4), dtype="float32")
offsets = np.zeros((vector_feature_count, 2), dtype=np.int32)
raster_data = None
for raster_index, raster_value in enumerate(in_rasters):
columns = {}
for stat in stats:
columns[prefixes[raster_index] + stat] = []
fits_in_memory = True
try:
raster_data = raster_to_array(
raster_value,
crop=[
overlap_aligned_offset[0],
overlap_aligned_offset[1],
overlap_aligned_rasterized_size[0],
overlap_aligned_rasterized_size[1],
],
)
except:
fits_in_memory = False
print("Raster does not fit in memory.. Doing IO for each feature.")
for n in range(vector_feature_count):
vector_feature = vector_layer.GetNextFeature()
rasterized_vector = None
if raster_index == 0:
try:
vector_geom = vector_feature.GetGeometryRef()
except:
vector_geom.Buffer(0)
Warning("Invalid geometry at : ", n)
if vector_geom is None:
raise Exception("Invalid geometry. Could not fix.")
feature_extent = vector_geom.GetEnvelope()
# Create temp layer
temp_vector_datasource = vector_driver.CreateDataSource(
f"vector_{n}")
temp_vector_layer = temp_vector_datasource.CreateLayer(
"temp_polygon", vector_projection, ogr.wkbPolygon)
temp_vector_layer.CreateFeature(vector_feature.Clone())
(
feature_aligned_extent,
feature_aligned_rasterized_size,
feature_aligned_offset,
) = align_extent(overlap_transform, feature_extent,
overlap_size)
rasterized_vector = rasterize_vector(
temp_vector_layer,
feature_aligned_extent,
feature_aligned_rasterized_size,
raster_projection,
)
rasterized_features.append(rasterized_vector)
offsets[n] = feature_aligned_offset
sizes[n] = feature_aligned_rasterized_size
if fits_in_memory is True:
cropped_raster = raster_data[offsets[n][1]:offsets[n][1] +
int(sizes[n][1]), # X
offsets[n][0]:offsets[n][0] +
int(sizes[n][0]), # Y
]
else:
cropped_raster = raster_to_array(
raster_value,
crop=[
overlap_aligned_offset[0] + offsets[n][0],
overlap_aligned_offset[1] + offsets[n][1],
int(sizes[n][0]),
int(sizes[n][1]),
],
)
if rasterized_features[n] is None:
for stat in stats:
field_name = f"{prefixes[raster_index]}{stat}"
vector_feature.SetField(field_name, None)
elif cropped_raster is None:
for stat in stats:
field_name = f"{prefixes[raster_index]}{stat}"
vector_feature.SetField(field_name, None)
else:
raster_data_masked = np.ma.masked_array(
cropped_raster,
mask=rasterized_features[n],
dtype="float32").compressed()
zonal_stats = calculate_array_stats(raster_data_masked,
stats_translated)
for index, stat in enumerate(stats):
field_name = f"{prefixes[raster_index]}{stat}"
vector_feature.SetField(field_name,
float(zonal_stats[index]))
vector_layer.SetFeature(vector_feature)
progress(n, vector_feature_count, name=prefixes[raster_index])
vector_layer.ResetReading()
vector_layer.CommitTransaction()
if output_vector is None:
return vector
return output_vector
def raster_to_grid(
raster: Union[str, gdal.Dataset],
grid: Union[str, ogr.DataSource],
out_dir: str,
use_field: Optional[str] = None,
generate_vrt: bool = True,
overwrite: bool = True,
process_layer: int = 0,
creation_options: list = [],
verbose: int = 1,
) -> Union[List[str], Tuple[Optional[List[str]], Optional[str]]]:
"""Clips a raster to a grid. Generate .vrt.
Returns:
The filepath for the newly created raster.
"""
type_check(raster, [str, gdal.Dataset], "raster")
type_check(grid, [str, ogr.DataSource], "grid")
type_check(out_dir, [str], "out_dir")
type_check(overwrite, [bool], "overwrite")
type_check(process_layer, [int], "process_layer")
type_check(creation_options, [list], "creation_options")
type_check(verbose, [int], "verbose")
use_grid = open_vector(grid)
grid_metadata = internal_vector_to_metadata(use_grid)
raster_metadata = raster_to_metadata(raster, create_geometry=True)
# Reproject raster if necessary.
if not raster_metadata["projection_osr"].IsSame(grid_metadata["projection_osr"]):
use_grid = reproject_vector(grid, raster_metadata["projection_osr"])
grid_metadata = internal_vector_to_metadata(use_grid)
if not isinstance(grid_metadata, dict):
raise Exception("Error while parsing metadata.")
# Only use the polygons in the grid that intersect the extent of the raster.
use_grid = intersect_vector(use_grid, raster_metadata["extent_datasource"])
ref = open_raster(raster)
use_grid = open_vector(use_grid)
layer = use_grid.GetLayer(process_layer)
feature_count = layer.GetFeatureCount()
raster_extent = raster_metadata["extent_ogr"]
filetype = path_to_ext(raster)
name = raster_metadata["name"]
geom_type = grid_metadata["layers"][process_layer]["geom_type_ogr"]
if use_field is not None:
if use_field not in grid_metadata["layers"][process_layer]["field_names"]:
names = grid_metadata["layers"][process_layer]["field_names"]
raise ValueError(
f"Requested field not found. Fields available are: {names}"
)
generated = []
# For the sake of good reporting - lets first establish how many features intersect
# the raster.
if verbose:
print("Finding intersections.")
intersections = 0
for _ in range(feature_count):
feature = layer.GetNextFeature()
geom = feature.GetGeometryRef()
if not ogr_bbox_intersects(raster_extent, geom.GetEnvelope()):
continue
intersections += 1
layer.ResetReading()
if verbose:
print(f"Found {intersections} intersections.")
if intersections == 0:
print("Warning: Found 0 intersections. Returning empty list.")
return ([], None)
# TODO: Replace this in gdal. 3.1
driver = ogr.GetDriverByName("Esri Shapefile")
clipped = 0
for _ in range(feature_count):
feature = layer.GetNextFeature()
geom = feature.GetGeometryRef()
if not ogr_bbox_intersects(raster_extent, geom.GetEnvelope()):
continue
if verbose == 1:
progress(clipped, intersections - 1, "clip_grid")
fid = feature.GetFID()
test_ds_path = f"/vsimem/grid_{uuid4().int}.shp"
test_ds = driver.CreateDataSource(test_ds_path)
test_ds_lyr = test_ds.CreateLayer(
"mem_layer_grid",
geom_type=geom_type,
srs=raster_metadata["projection_osr"],
)
test_ds_lyr.CreateFeature(feature.Clone())
test_ds.FlushCache()
out_name = None
if use_field is not None:
out_name = f"{out_dir}{feature.GetField(use_field)}{filetype}"
else:
out_name = f"{out_dir}{name}_{fid}{filetype}"
clip_raster(
ref,
test_ds_path,
out_path=out_name,
adjust_bbox=True,
crop_to_geom=True,
all_touch=False,
postfix="",
prefix="",
creation_options=default_options(creation_options),
verbose=0,
)
generated.append(out_name)
clipped += 1
if generate_vrt:
vrt_name = f"{out_dir}{name}.vrt"
stack_rasters_vrt(generated, vrt_name, seperate=False)
return (generated, vrt_name)
return generated