def get_tiles(datasource, width=64, height=64, fold=1, extension=16):
"""Function to create the product matrix for the tiles from the big picture.
Width & Height corresponds to the tile dimensions, whereas fold refers to the overlap of the tiles.
1 = no overlap, 2 = overlapping by a half tile, and so on."""
ncols, nrows = datasource.meta['width'], datasource.meta['height']
offsets = product(range(0, ncols, width//fold), range(0, nrows, height//fold)) # control the level of overlap
big_window = windows.Window(col_off=0, row_off=0, width=ncols, height=nrows)
# defining window and transfor functions (original and extended)
for col_off, row_off in offsets:
# creating the original tile
window_original = windows.Window(col_off=col_off, row_off=row_off, width=width, height=height).intersection(big_window)
transform_original = windows.transform(window_original, datasource.transform)
# creating the extended boundary tile
col_off_extended = col_off - extension
row_off_extended = row_off - extension
width_extended = width + extension*2
height_extended = height + extension*2
# windowing with the extended tile
window_extended = windows.Window(col_off = col_off_extended,
row_off = row_off_extended,
width = width_extended,
height = height_extended).intersection(big_window)
transform_extended = windows.transform(window_extended, datasource.transform)
yield window_original, transform_original, window_extended, transform_extended
def get_tiles(src, nr_tiles):
if nr_tiles < 36:
scale = int(np.ceil(np.sqrt(nr_tiles)))
size = int(src.meta['width'] / scale)
#get original image dimension
ncols, nrows = src.meta['width'], src.meta['height']
#define offsets (iterator) based on specified tile size
offsets = product(range(0, ncols, size), range(0, nrows, size))
#create a large tile from original image dimension
big_window = windows.Window(col_off=0,
row_off=0,
width=ncols,
height=nrows)
#find intersection with the large tile and get the tile dimension and affine
tiles = []
for col_off, row_off in offsets:
tile = windows.Window(col_off=col_off,
row_off=row_off,
width=size,
height=size).intersection(big_window)
tile_affine = windows.transform(tile, src.transform)
tiles.append((tile, tile_affine))
return tiles
else:
print('Try number of tiles < 36')
def get_tile(self, level, tile_y, tile_x):
"""
Returns (id_x, id_y), Window, Transformation
:param tile_x: int
:param level: int
:param tile_y: int
:return tuple
"""
level = int(level)
tile_y = int(tile_y)
tile_x = int(tile_x)
ds = self.get_dataset(level)
width = ds.width
height = ds.height
geotransform = self.get_transform(level)
y_offset = tile_y * self.TileHeight
x_offset = tile_x * self.TileWidth
y_size = min(self.TileWidth, height - y_offset)
x_size = min(self.TileHeight, width - x_offset)
w = Window(x_offset, y_offset, x_size, y_size)
t = transform(w, geotransform)
return (tile_x + 1, tile_y + 1), w, t
def get_tiles(dataset, tile_a, tile_b):
"""Creates rasterio.windows for a given Band with the size of
tile_a x tile_b
:parameter:
Open Source,
tile-width in m,
tile-height in m,
:returns:
rasterio window """
# calculate Width and Height as Distance from Origin
tile_x, tile_y = (dataset.bounds.left + tile_a,
dataset.bounds.top - tile_b)
height, width = dataset.index(tile_x, tile_y)
# get max rows and cols of dataset
nols, nrows = dataset.meta['width'], dataset.meta['height']
# create offset for the window processing
offsets = product(range(0, nols, width), range(0, nrows, height))
# create big_window around the whole dataset
big_window = windows.Window(col_off=0, row_off=0, width=nols, height=nrows)
# create custom set blocks
for col_off, row_off in offsets:
# get windows with the custom parameters
# until it intersects with
# the boundaries of the source dataset
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
transform = windows.transform(window, dataset.transform)
yield window, transform
def __init__(self,
filepath,
resolution=1,
min_lat=None,
max_lat=None,
min_long=None,
max_long=None):
self.src = rasterio.open(filepath)
self.min_lat = min_lat
self.max_lat = max_lat
self.min_long = min_long
self.max_long = max_long
self.resolution = resolution
self.window = self._calc_window()
if min_lat is None:
self.transform_window = self.src.transform
self.band = self.src.read(1)
else:
logging.warn('there are bugs in windowed transformation!')
self.transform_window = windows.transform(self.window,
self.src.transform)
self.band = self.src.read(1, window=self.window)
self.transform, self.width, self.height = self._calc_transform()
self.array = self._reproject()
def get_tiles(ds, width, height):
"""
Takes as input the a tile and returns chips.
==========================================
:width: Desired width of each chip.
:height: Desired height of each chip.
:out_path: Desired output file storage folder.
:in_path: Folder where the input tile is stored.
:input_filename: Name of the input tile
:output_filename: Desired output file pattern
===========================================
"""
ncols = ds.meta["width"]
nrows = ds.meta["height"]
offsets = product(range(0, ncols, width), range(0, nrows, height))
big_window = windows.Window(col_off=0,
row_off=0,
width=ncols,
height=nrows)
for col_off, row_off in offsets:
window = windows.Window(
col_off=col_off,
row_off=row_off,
width=width,
height=height,
).intersection(big_window)
transform = windows.transform(window, ds.transform)
yield window, transform
def split(infile, width, height=None):
dataset = rasterio.open(infile)
height = height or width
raw_width = dataset.width
raw_height = dataset.height
raw_window = Window(0, 0, raw_width, raw_height)
filename = dataset.name
outfiles = []
for col_off in range(0, raw_width, width):
for row_off in range(0, raw_height, height):
window = Window(col_off, row_off, width, height).intersection(raw_window)
meta = dataset.meta.copy()
meta.update({
'width': window.width,
'height': window.height,
'transform': transform(window, dataset.transform)
})
outfile = os.path.splitext(filename)[0] + "_{}_{}_{}_{}".format(col_off, row_off, window.width, window.height) + os.path.splitext(filename)[1]
with rasterio.open(outfile, 'w', **meta) as dst:
dst.write(dataset.read(window=window))
# Copy existing band descriptions to the splitted images
descriptions = rasterio.open(infile).descriptions
for band, desc in enumerate(descriptions, start=1):
if desc is not None:
dst.set_band_description(band, desc)
outfiles.append(outfile)
return outfiles
def get_rasters_merged(raster_file1,
raster_file2,
output_file,
tmp_dir,
grid_blocks=5):
p = Path(tmp_dir)
tmp_files = [str(f) for f in list(p.glob('tmp*.tif'))]
for f in tmp_files:
os.remove(f)
windows = make_windows(raster_file1, grid_blocks=grid_blocks)
pbar = tqdm(enumerate(windows), total=len(windows))
for idx, window in pbar:
raster1 = rio.open(raster_file1).read(1, window=window)
raster2 = rio.open(raster_file2).read(1, window=window)
result = np.maximum(raster1, raster2)
# Save
image_src = raster_file1
tmp_file = tmp_dir + 'tmp{}.tif'.format(idx)
tfm = transform(window, transform=rio.open(image_src).transform)
save_predictions_window(result, image_src, tmp_file, window, tfm)
stitch(output_file, tmp_dir)
def get_tiles(ds, width, height): nols, nrows = ds.meta['width'], ds.meta['height'] offsets = product(range(0, nols, width), range(0, nrows, height)) big_window = windows.Window(col_off=0, row_off=0, width=nols, height=nrows) for col_off, row_off in offsets: window =windows.Window(col_off=col_off, row_off=row_off, width=width, height=height).intersection(big_window) transform = windows.transform(window, ds.transform) yield window, transform
def generate_tiles(input_tif, width=500, height=500):
n_cols, n_rows = input_tif.meta['width'], input_tif.meta['height']
offsets = product(range(0, n_cols, width), range(0, n_rows, height))
full_window = windows.Window(col_off=0, row_off=0, width=n_cols, height=n_rows)
for col_off, row_off in offsets:
window=windows.Window(col_off=col_off, row_off=row_off, width=width, height=height).intersection(full_window)
transform = windows.transform(window, input_tif.transform)
yield window, transform
def _compute_image_stats_chunked(dataset: 'DatasetReader') -> Optional[Dict[str, Any]]:
"""Compute statistics for the given rasterio dataset by looping over chunks."""
from rasterio import features, warp, windows
from shapely import geometry
total_count = valid_data_count = 0
tdigest = TDigest()
sstats = SummaryStats()
convex_hull = geometry.Polygon()
block_windows = [w for _, w in dataset.block_windows(1)]
for w in block_windows:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', message='invalid value encountered.*')
block_data = dataset.read(1, window=w, masked=True)
# handle NaNs for float rasters
block_data = np.ma.masked_invalid(block_data, copy=False)
total_count += int(block_data.size)
valid_data = block_data.compressed()
if valid_data.size == 0:
continue
valid_data_count += int(valid_data.size)
if np.any(block_data.mask):
hull_candidates = RasterDriver._hull_candidate_mask(~block_data.mask)
hull_shapes = [geometry.shape(s) for s, _ in features.shapes(
np.ones(hull_candidates.shape, 'uint8'),
mask=hull_candidates,
transform=windows.transform(w, dataset.transform)
)]
else:
w, s, e, n = windows.bounds(w, dataset.transform)
hull_shapes = [geometry.Polygon([(w, s), (e, s), (e, n), (w, n)])]
convex_hull = geometry.MultiPolygon([convex_hull, *hull_shapes]).convex_hull
tdigest.update(valid_data)
sstats.update(valid_data)
if sstats.count() == 0:
return None
convex_hull_wgs = warp.transform_geom(
dataset.crs, 'epsg:4326', geometry.mapping(convex_hull)
)
return {
'valid_percentage': valid_data_count / total_count * 100,
'range': (sstats.min(), sstats.max()),
'mean': sstats.mean(),
'stdev': sstats.std(),
'percentiles': tdigest.quantile(np.arange(0.01, 1, 0.01)),
'convex_hull': convex_hull_wgs
}
def _get_save_profile(self, dataset: DatasetReader, window: Window):
kwargs = dataset.meta.copy()
kwargs.update({
"driver": self.driver,
"height": window.height,
"width": window.width,
"transform": transform(window, dataset.transform),
"compress": "lzw",
})
return kwargs
def get_preds_windowing(area,
area_dict,
model,
tmp_dir,
best_features,
output,
grid_blocks=5,
threshold=0):
# Delete tmp files from previous run
if Path(output).is_file():
os.remove(output)
p = Path(tmp_dir)
tmp_files = [str(f) for f in list(p.glob('tmp*.tif'))]
for f in tmp_files:
os.remove(f)
# Read bands
src_file = area_dict[area]['images'][0]
windows = make_windows(src_file, grid_blocks=grid_blocks)
pbar = tqdm(enumerate(windows), total=len(windows))
for idx, window in pbar:
pbar.set_description('Processing {}...'.format(area))
df_bands = read_bands_window(area_dict, area, window=window)
df_inds = read_inds_window(area_dict, area, window=window)
df_test = pd.concat((df_bands, df_inds), axis=1)
df_test = rename_ind_cols(df_test)
df_test = df_test.replace([np.inf, -np.inf], 0)
# Prediction
X_test = df_test[best_features].fillna(0)
all_zeroes = (X_test.iloc[:, :-1].sum(axis=1) == 0)
data = X_test
features = best_features
# Prettify Tiff
preds = model.predict_proba(data)[:, 1]
if threshold > 0:
preds[(preds < threshold)] = 0
preds[all_zeroes] = -1
# Save
image_src = src_file
output_file = tmp_dir + 'tmp{}.tif'.format(idx)
tfm = transform(window, transform=rio.open(src_file).transform)
save_predictions_window(preds, image_src, output_file, window, tfm)
#print('Saving to {}...'.format(output))
stitch(output, tmp_dir)
def get_tiles(ds, width=256, height=256):
nols, nrows = ds.meta['width'], ds.meta['height']
offsets = product(range(0, nols, int(width / 2)),
range(0, nrows, int(height / 2)))
big_window = windows.Window(col_off=0, row_off=0, width=nols, height=nrows)
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
if window.width == width and window.height == height:
transform = windows.transform(window, ds.transform)
yield window, transform
def _get_window_transform(width, height):
num_rows, num_cols = src.meta['height'], src.meta['width']
offsets = itertools.product(range(0, num_cols, width),
range(0, num_rows, height))
big_window = windows.Window(col_off=0,
row_off=0,
width=num_cols,
height=num_rows)
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
transform = windows.transform(window, src.transform)
yield window, transform
def build_window(in_src, in_xy_ul, in_chip_size, in_chip_stride):
out_window = windows.Window(col_off=in_xy_ul[0],
row_off=in_xy_ul[1],
width=in_chip_size,
height=in_chip_size)
out_win_transform = windows.transform(out_window, in_src.transform)
col_id = in_xy_ul[1] // in_chip_stride
row_id = in_xy_ul[0] // in_chip_stride
out_win_id = f'{col_id}_{row_id}'
out_win_bounds = windows.bounds(out_window, out_win_transform)
return out_window, out_win_transform, out_win_bounds, out_win_id
def window_transform(self, window):
"""Get the affine transform for a dataset window.
Parameters
----------
window: tuple
Dataset window tuple
Returns
-------
transform: Affine
The affine transform matrix for the given window
"""
transform = guard_transform(self.transform)
return windows.transform(window, transform)
def _get_window_transform(width, height, transform, num_tile_subdivisions):
dst_width = width // num_tile_subdivisions
dst_height = height // num_tile_subdivisions
offsets = itertools.product(range(0, width, dst_width),
range(0, height, dst_height))
big_window = windows.Window(col_off=0,
row_off=0,
width=width,
height=height)
for col_off, row_off in offsets:
dst_window = windows.Window(col_off=col_off,
row_off=row_off,
width=dst_width,
height=dst_height).intersection(big_window)
dst_transform = windows.transform(dst_window, transform)
yield dst_window, dst_transform
def window_transform(self, window):
"""Get the affine transform for a dataset window.
Parameters
----------
window: tuple
Dataset window tuple
Returns
-------
transform: Affine
The affine transform matrix for the given window
"""
transform = guard_transform(self.transform)
return windows.transform(window, transform)
def test_window_transform_function(path_rgb_byte_tif):
with rasterio.open(path_rgb_byte_tif) as src:
assert transform(((0, None), (0, None)), src.transform) == src.transform
assert transform(((None, None), (None, None)), src.transform) == src.transform
assert transform(
((1, None), (1, None)), src.transform).c == src.bounds.left + src.res[0]
assert transform(
((1, None), (1, None)), src.transform).f == src.bounds.top - src.res[1]
assert transform(
((-1, None), (-1, None)), src.transform).c == src.bounds.left - src.res[0]
assert transform(
((-1, None), (-1, None)), src.transform).f == src.bounds.top + src.res[1]
assert transform(
Window(-1, -1, src.width + 1, src.height + 1),
src.transform).f == src.bounds.top + src.res[1]
def extract_raster_window(in_ds, upperleft, lowerright):
meta = in_ds.meta.copy()
start_row, start_col = in_ds.index(upperleft.x, upperleft.y)
stop_row, stop_col = in_ds.index(lowerright.x, lowerright.y)
width = stop_col - start_col
height = stop_row - start_row
zone = Window(start_col, start_row, width, height)
data = in_ds.read(1, window=zone)
meta.update({
"driver": "GTiff",
"height": zone.height,
"width": zone.width,
"transform": transform(zone, in_ds.transform),
})
return data, meta, zone
def test_window_transform_function(path_rgb_byte_tif):
with rasterio.open(path_rgb_byte_tif) as src:
assert transform(((0, None), (0, None)), src.transform) == src.transform
assert transform(((None, None), (None, None)), src.transform) == src.transform
assert transform(
((1, None), (1, None)), src.transform).c == src.bounds.left + src.res[0]
assert transform(
((1, None), (1, None)), src.transform).f == src.bounds.top - src.res[1]
assert transform(
((-1, None), (-1, None)), src.transform).c == src.bounds.left - src.res[0]
assert transform(
((-1, None), (-1, None)), src.transform).f == src.bounds.top + src.res[1]
assert transform(
Window(-1, -1, src.width + 1, src.height + 1),
src.transform).f == src.bounds.top + src.res[1]
def get_tiles(ds, width=256, height=256):
out_window = []
out_transform = []
ncols, nrows = ds.meta['width'], ds.meta['height']
offsets = product(range(0, ncols, width), range(0, nrows, height))
big_window = windows.Window(col_off=0,
row_off=0,
width=ncols,
height=nrows)
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
out_window.append(window)
out_transform.append(windows.transform(window, ds.transform))
return out_window, out_transform
def get_tiles(ds, width, height):
"""
Create chip tiles generator
:param ds: rasterio dataset
:param width: tile width
:param height: tile height
:return: generator of rasterio windows and transforms for each tile to be created
"""
intersect_window = get_intersect_win(ds)
offsets = product(range(intersect_window.col_off, intersect_window.width + intersect_window.col_off, width),
range(intersect_window.row_off, intersect_window.height + intersect_window.row_off, height))
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off, row_off=row_off, width=width, height=height).intersection(intersect_window)
transform = windows.transform(window, ds.transform)
yield window, transform
def test_window_transform_function():
with rasterio.open('tests/data/RGB.byte.tif') as src:
assert transform(((0, None), (0, None)), src.transform) == src.transform
assert transform(((None, None), (None, None)), src.transform) == src.transform
assert transform(
((1, None), (1, None)), src.transform).c == src.bounds.left + src.res[0]
assert transform(
((1, None), (1, None)), src.transform).f == src.bounds.top - src.res[1]
assert transform(
((-1, None), (-1, None)), src.transform).c == src.bounds.left - src.res[0]
assert transform(
((-1, None), (-1, None)), src.transform).f == src.bounds.top + src.res[1]
def test_window_transform_function():
with rasterio.open('tests/data/RGB.byte.tif') as src:
assert transform(((0, None), (0, None)), src.transform) == src.transform
assert transform(((None, None), (None, None)), src.transform) == src.transform
assert transform(
((1, None), (1, None)), src.transform).c == src.bounds.left + src.res[0]
assert transform(
((1, None), (1, None)), src.transform).f == src.bounds.top - src.res[1]
assert transform(
((-1, None), (-1, None)), src.transform).c == src.bounds.left - src.res[0]
assert transform(
((-1, None), (-1, None)), src.transform).f == src.bounds.top + src.res[1]
def get_windows(self, raster, width, height, overlap=None):
"""
Produces regularly spaced tiles (instances of rasterio.window.Window)
of size [width, height] pixels from rasterio DataSetReader (image file opened
with rasterio.open) with overlap
width: width of tiles (pixels)
height: height of tiles (pixels)
overlap: overlap of tiles in each dimension (relative units, range [0 1[)
"""
# check overlap
if overlap is None:
overlap = 0.0
else:
assert (overlap >= 0.0) and (overlap <
1.0), "overlap must be in [0 1["
width_masterImg, height_masterImg = raster.meta['width'], raster.meta[
'height']
# check that tiles are within master image
assert (width <= width_masterImg) and (
height <= height_masterImg), "tiles are too large for image"
# produce a list of regularly spaced horizontal offsets such that all tiles produced
# from it fit into the master image, plus one offset which ensures that the right edge
# of the last tile is identical to the right edge of the master window (the implication
# is that the last tile has a different degree of overlap with its neighbor than the other
# images)
offsets_horiz = list(
range(0, width_masterImg, round(width * (1.0 - overlap))))
offsets_horiz[-1] = width_masterImg - width
# same for vertical offsets
offsets_vert = list(
range(0, height_masterImg, round(height * (1.0 - overlap))))
offsets_vert[-1] = height_masterImg - height
# construct iterator
offsets = product(offsets_horiz, offsets_vert)
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height)
transform = windows.transform(window, raster.transform)
yield window, transform
def get_tile(self, file, width=512, height=512, col_off=0, row_off=0):
with rio.open(file, dtype='float32') as data:
meta = data.meta.copy()
return data.read().transpose(1, 2, 0).astype(rio.float32), meta
ncols, nrows = meta['width'], meta['height']
offsets = product(range(0, ncols, width), range(0, nrows, height))
big_window = wnd.Window(col_off=0, row_off=0, width=ncols, height=nrows)
window = wnd.Window(col_off=col_off * width, row_off=row_off * height,
width=width, height=height).intersection(big_window)
transform = wnd.transform(window, data.transform)
meta['transform'] = transform
meta['width'], meta['height'] = window.width, window.height
return data.read(window=window).transpose(1, 2, 0).astype(rio.float32), meta
def get_window(file_path, width=512, height=512, col_off=0, row_off=0):
with rio.open(file_path) as data:
meta = data.meta.copy()
ncols, nrows = meta['width'], meta['height']
offsets = product(range(0, ncols, width), range(0, nrows, height))
full_image = wnd.Window(col_off=0, row_off=0, width=ncols, height=nrows)
window = wnd.Window(col_off=col_off * width, row_off=row_off * height,
width=width, height=height).intersection(full_image)
transform = wnd.transform(window, meta['transform'])
meta['transform'] = transform
meta['width'], meta['height'] = window.width, window.height
return data.read(window=window).transpose(1, 2, 0), meta
def get_mask(mask_type=mask_types[0]):
assert mask_type in mask_types, "mask type must be OPAQUE or CIRRUS"
fill_value = {m: i + 1 for i, m in enumerate(mask_types)}
n_polys = np.sum([
poly["attributes"]["maskType"] == mask_type
for poly in poly_list
])
msk = np.zeros(shape=(nrows, ncols), dtype=np.float32)
if n_polys > 0:
multi_polygon = MultiPolygon([
poly["geometry"] for poly in poly_list
if poly["attributes"]["maskType"] == mask_type
]).buffer(0)
bounds = multi_polygon.bounds
bbox2read = coords.BoundingBox(*bounds)
window_read = windows.from_bounds(*bbox2read, transform_)
slice_read = tuple(
slice(int(round(s.start)), int(round(s.stop)))
for s in window_read.toslices())
out_shape = tuple([s.stop - s.start for s in slice_read])
transform_slice = windows.transform(window_read, transform_)
shapes = [({
"type":
"Polygon",
"coordinates": [
np.stack([
p_elem["geometry"].exterior.xy[0],
p_elem["geometry"].exterior.xy[1]
],
axis=1).tolist()
]
}, fill_value[mask_type]) for p_elem in poly_list
if p_elem["attributes"]['maskType'] == mask_type]
sub_msk = features.rasterize(shapes=shapes,
fill=0,
out_shape=out_shape,
dtype=np.float32,
transform=transform_slice)
msk[slice_read] = sub_msk
return msk
def get_tiles(ds, tile_a, tile_b):
"""Tiles a band into blocks with the dimension of tile_a x tile_b
:parameter: Band, tile-width and tile-height
:returns: rasterio window """
# calculate Width and Height as Distance from Origin
x, y = (ds.bounds.left + tile_a, ds.bounds.top - tile_b)
height, width = ds.index(x, y)
nols, nrows = ds.meta['width'], ds.meta['height']
offsets = product(range(0, nols, width), range(0, nrows, height))
big_window = windows.Window(col_off=0, row_off=0, width=nols, height=nrows)
# create custom set blocks
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
transform = windows.transform(window, ds.transform)
yield window, transform
def get_patch_windows(img_size: ImageSize, patch_size: PatchSize, patch_residue: PatchResidue = None, img_transform=None) -> Iterator:
img_w, img_h = img_size
big_window = windows.Window(
col_off=0, row_off=0, width=img_w, height=img_h)
offsets = get_patch_offsets((img_w, img_h), patch_size, patch_residue)
patch_w, patch_h = normalize_patch_size(patch_size)
for col_off, row_off in offsets:
window = windows.Window(
col_off=col_off,
row_off=row_off,
width=patch_w,
height=patch_h
).intersection(big_window)
if img_transform:
transform = windows.transform(window, img_transform)
yield window, transform
else:
yield window
def get_tiles(dataset,
width=utils.constants.width,
height=utils.constants.height):
"""
Implementation from https://github.com/jesseniagonzalezv/App_segmentation_water_bodies
:param dataset: data reader
:param width: patch expected width
:param height: patch expected height
:return: crop windows and Affine transform
"""
nols, nrows = dataset.meta['width'], dataset.meta['height']
offsets = product(range(0, nols, width), range(0, nrows, height))
big_window = windows.Window(col_off=0, row_off=0, width=nols, height=nrows)
for col_off, row_off in offsets:
window = windows.Window(col_off=col_off,
row_off=row_off,
width=width,
height=height).intersection(big_window)
transform = windows.transform(window, dataset.transform)
yield window, transform
def test_reproject_view():
"""Source views are reprojected properly"""
with rasterio.open("tests/data/RGB.byte.tif") as src:
source = src.read(1)
window = windows.Window(100, 100, 500, 500)
# window = windows.get_data_window(source)
reduced_array = source[window.toslices()]
reduced_transform = windows.transform(window, src.transform)
# Assert that we're working with a view.
assert reduced_array.base is source
dst_crs = dict(
proj="merc",
a=6378137,
b=6378137,
lat_ts=0.0,
lon_0=0.0,
x_0=0.0,
y_0=0,
k=1.0,
units="m",
nadgrids="@null",
wktext=True,
no_defs=True,
)
out = np.empty(src.shape, dtype=np.uint8)
reproject(
reduced_array,
out,
src_transform=reduced_transform,
src_crs=src.crs,
dst_transform=DST_TRANSFORM,
dst_crs=dst_crs,
resampling=Resampling.nearest,
)
assert (out > 0).sum() == 299199