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__init__.py
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__init__.py
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"""
"""
from affine import Affine
class WindowFromSlice(object):
def __getitem__(self, yx):
""" Translate numpy-like slices to rasterio window tuples.
"""
assert isinstance(yx, tuple) and len(yx) == 2
y, x = yx
return ((0 if y.start is None else y.start, y.stop),
(0 if x.start is None else x.start, x.stop))
w_ = WindowFromSlice()
def web_geobox(zoom, tx, ty, tile_size=256):
"""Construct geobox for a given web-tile.
Tile indexes should be the same as google maps.
http://www.maptiler.org/google-maps-coordinates-tile-bounds-projection/
"""
from datacube.utils.geometry import CRS, GeoBox
from math import pi
R = 6378137
origin = pi * R
res0 = 2 * pi * R / tile_size
res = res0*(2**(-zoom))
tsz = 2 * pi * R * (2**(-zoom)) # res*tile_size
# maps pixel coord to meters in EPSG:3857
#
transform = Affine(res, 0, tx*tsz - origin,
0, -res, origin - ty*tsz)
return GeoBox(tile_size, tile_size, transform, CRS('epsg:3857'))
def web_tile_zoom_out(zxy):
""" Compute tile at lower zoom level that contains this tile.
"""
z, x, y = zxy
return (z-1, x//2, y//2)
def web_tile_zoom_in(zxy, quadrant=None):
""" Compute tile at higher zoom level that is contained by this tile.
zxy - zoom, x, y
quadrant - one of tl, tr, bl, br
"""
z, x, y = zxy
off_x, off_y = dict(tl=(0, 0), tr=(1, 0),
bl=(0, 1), br=(1, 1)).get(quadrant, (0, 0))
return (z+1, x*2 + off_x, y*2 + off_y)
def polygon_path(x, y=None):
"""A little bit like numpy.meshgrid, except returns only boundary values and
limited to 2d case only.
Examples:
[0,1], [3,4] =>
array([[0, 1, 1, 0, 0],
[3, 3, 4, 4, 3]])
[0,1] =>
array([[0, 1, 1, 0, 0],
[0, 0, 1, 1, 0]])
"""
import numpy as np
if y is None:
y = x
return np.vstack([
np.vstack([x, np.full_like(x, y[0])]).T,
np.vstack([np.full_like(y, x[-1]), y]).T[1:],
np.vstack([x, np.full_like(x, y[-1])]).T[::-1][1:],
np.vstack([np.full_like(y, x[0]), y]).T[::-1][1:]]).T
def gbox_boundary(gbox, pts_per_side=16):
"""Return points in pixel space along the perimeter of a GeoBox, or a 2d array.
"""
from numpy import linspace
H, W = gbox.shape[:2]
xx = linspace(0, W, pts_per_side, dtype='float32')
yy = linspace(0, H, pts_per_side, dtype='float32')
return polygon_path(xx, yy).T[:-1]
def decompose_rws(A):
"""Compute decomposition Affine matrix sans translation into Rotation Shear and Scale.
Note: that there are ambiguities for negative scales.
Example: R(90)*S(1,1) == R(-90)*S(-1,-1),
(R*(-I))*((-I)*S) == R*S
A = R W S
Where:
R [ca -sa] W [1, w] S [sx, 0]
[sa ca] [0, 1] [ 0, sy]
"""
from numpy import diag, asarray
from numpy.linalg import cholesky, det, inv
if isinstance(A, Affine):
def to_affine(m, t=(0, 0)):
a, b, d, e = m.ravel()
c, f = t
return Affine(a, b, c,
d, e, f)
(a, b, c,
d, e, f,
*_) = A
R, W, S = decompose_rws(asarray([[a, b],
[d, e]], dtype='float64'))
return to_affine(R, (c, f)), to_affine(W), to_affine(S)
assert A.shape == (2, 2)
WS = cholesky(A.T @ A).T
R = A @ inv(WS)
if det(R) < 0:
R[:, -1] *= -1
WS[-1, :] *= -1
ss = diag(WS)
S = diag(ss)
W = WS @ diag(1.0/ss)
return R, W, S
def affine_from_pts(X, Y):
""" Given points X,Y compute A, such that: Y = A*X.
Needs at least 3 points.
"""
from numpy import ones, vstack
from numpy.linalg import lstsq
assert len(X) == len(Y)
assert len(X) >= 3
n = len(X)
XX = ones((n, 3), dtype='float64')
YY = vstack(Y)
for i, x in enumerate(X):
XX[i, :2] = x
mm, *_ = lstsq(XX, YY, rcond=-1)
a, d, b, e, c, f = mm.ravel()
return Affine(a, b, c,
d, e, f)
def get_scale_at_point(pt, tr, r=None):
""" Given an arbitrary locally linear transform estimate scale change around a point.
1. Approximate Y = tr(X) as Y = A*X+t in the neighbourhood of pt, for X,Y in R2
2. Extract scale components of A
pt - estimate transform around this point
r - radius around the point (default 1)
tr - List((x,y)) -> List((x,y))
takes list of 2-d points on input and outputs same length list of 2d on output
"""
pts0 = [(0, 0), (-1, 0), (0, -1), (1, 0), (0, 1)]
x0, y0 = pt
if r is None:
XX = [(float(x+x0), float(y+y0)) for x, y in pts0]
else:
XX = [(float(x*r+x0), float(y*r+y0)) for x, y in pts0]
YY = tr(XX)
A = affine_from_pts(XX, YY)
_, _, S = decompose_rws(A)
return (abs(S.a), abs(S.e))
def native_pix_transform(src, dst):
"""
direction: from src to dst
.back: goes the other way
"""
from types import SimpleNamespace
from osgeo import osr
# TODO: special case CRS_in == CRS_out
#
_in = SimpleNamespace(crs=src.crs._crs, A=src.transform)
_out = SimpleNamespace(crs=dst.crs._crs, A=dst.transform)
_fwd = osr.CoordinateTransformation(_in.crs, _out.crs)
_bwd = osr.CoordinateTransformation(_out.crs, _in.crs)
_fwd = (_in.A, _fwd, ~_out.A)
_bwd = (_out.A, _bwd, ~_in.A)
def transform(pts, params):
A, f, B = params
return [B*pt[:2] for pt in f.TransformPoints([A*pt[:2] for pt in pts])]
def tr(pts):
return transform(pts, _fwd)
tr.back = lambda pts: transform(pts, _bwd)
return tr
def scaled_down_geobox(src_geobox, scaler: int):
"""Given a source geobox and integer scaler compute geobox of a scaled down image.
Output geobox will be padded when shape is not a multiple of scaler.
Example: 5x4, scaler=2 -> 3x2
NOTE: here we assume that pixel coordinates are 0,0 at the top-left
corner of a top-left pixel.
"""
from datacube.utils.geometry import GeoBox
assert scaler > 1
H, W = [X//scaler + (1 if X % scaler else 0)
for X in src_geobox.shape]
# Since 0,0 is at the corner of a pixel, not center, there is no
# translation between pixel plane coords due to scaling
A = src_geobox.transform * Affine.scale(scaler, scaler)
return GeoBox(W, H, A, src_geobox.crs)
def align_down(x, align):
return x - (x % align)
def align_up(x, align):
return align_down(x+(align-1), align)
def scaled_down_roi(roi, scale: int):
return tuple(slice(s.start//scale,
align_up(s.stop, scale)//scale) for s in roi)
def scaled_up_roi(roi, scale: int, shape=None):
roi = tuple(slice(s.start*scale,
s.stop*scale) for s in roi)
if shape is not None:
roi = tuple(slice(min(dim, s.start),
min(dim, s.stop))
for s, dim in zip(roi, shape))
return roi
def scaled_down_shape(shape, scale: int):
return tuple(align_up(s, scale)//scale for s in shape)
def roi_shape(roi):
def slice_dim(s):
return s.stop if s.start is None else s.stop - s.start
return tuple(slice_dim(s) for s in roi)
def roi_is_empty(roi):
return any(d <= 0 for d in roi_shape(roi))
def pick_overview(scale, overviews):
prev = 1
for v in sorted(overviews):
if v > scale:
return prev
prev = v
return prev
def compute_reproject_roi(src, dst, padding=1, align=None):
""" Compute ROI of src to read and read scale.
"""
import numpy as np
pts_per_side = 5
tr = native_pix_transform(src, dst)
XY = np.vstack(tr.back(gbox_boundary(dst, pts_per_side)))
_in = np.floor(XY.min(axis=0)).astype('int32') - padding
_out = np.ceil(XY.max(axis=0)).astype('int32') + padding
if align is not None:
_in = align_down(_in, align)
_out = align_up(_out, align)
xx = np.asarray([_in[0], _out[0]])
yy = np.asarray([_in[1], _out[1]])
xx = np.clip(xx, 0, src.width, out=xx)
yy = np.clip(yy, 0, src.height, out=yy)
center_pt = xx.mean(), yy.mean()
scale = min(1/s for s in get_scale_at_point(center_pt, tr))
return (slice(yy[0], yy[1]), slice(xx[0], xx[1])), scale
def rio_default_transform(src, dst_crs):
""" Wrapper for rasterio.warp.calculate_default_transform
that accepts GeoBox objects
"""
from rasterio.warp import calculate_default_transform
bb = src.extent.boundingbox
return calculate_default_transform(str(src.crs),
str(dst_crs),
src.width,
src.height,
left=bb.left,
right=bb.right,
top=bb.top,
bottom=bb.bottom)
def rio_crs_to_odc(crs):
from datacube.utils.geometry import CRS
if crs.is_epsg_code:
return CRS('epsg:{}'.format(crs.to_epsg()))
return CRS(crs.wkt)
def rio_geobox(src):
from datacube.utils.geometry import GeoBox
return GeoBox(src.width,
src.height,
src.transform,
rio_crs_to_odc(src.crs))
def _empty_image(shape, src, band, nodata=None):
import numpy as np
if isinstance(band, int):
b0 = band - 1
else:
b0 = band[0] - 1
shape = (len(band), *shape)
dtype = np.dtype(src.dtypes[b0])
if nodata is None:
nodata = src.nodatavals[b0]
if nodata is None:
nodata = np.nan if dtype.char == 'f' else 0
if nodata == 0:
return np.zeros(shape, dtype=dtype)
out = np.empty(shape, dtype=dtype)
out[:] = nodata
return out
def resolve_nodata(src, band, fallback=None, override=None):
"""Figure out what value to use for nodata given a band and fallback/override
settings
"""
if override is not None:
return override
band0 = band if isinstance(band, int) else band[0]
nodata = src.nodatavals[band0 - 1]
if nodata is None:
return fallback
return nodata
def read_with_reproject(src,
dst_geobox,
band=1,
resampling=None,
dst_nodata=None,
src_nodata_fallback=None,
src_nodata_override=None):
"""Two stage reproject: scaling read then re-project.
src - opened rasterio file handle
dst_geobox - GeoBox (from datacube) of the resulting image
crs, transform, shape
band - Which band to read (rasterio, 1-based index), could also be a
list/tuple if multiple bands are to be read
resampling - rasterio resampling enumeation or None for default NN
dst_nodata - when set use that instead of defaulting to src nodata value
src_nodata_fallback - nodata value to use if src file is missing nodata value
src_nodata_override - when set use that instead of what's in the file,
useful when nodata metadata is incorrect in the file
but correct value is available out of band.
returns:
numpy array of the same shape as dst_geobox and the same dtype as src image
"""
from rasterio.warp import reproject
import numpy as np
src_geobox = rio_geobox(src)
roi, scale = compute_reproject_roi(src_geobox,
dst_geobox,
padding=2,
align=64)
band0 = band if isinstance(band, int) else band[0]
src_nodata = resolve_nodata(src, band,
fallback=src_nodata_fallback,
override=src_nodata_override)
if dst_nodata is None:
dst_nodata = src_nodata
if roi_is_empty(roi):
return _empty_image(dst_geobox.shape, src, band, nodata=dst_nodata)
overviews = src.overviews(band0)
ovr_scale = pick_overview(scale, overviews)
if ovr_scale > 1:
ovr_geobox = scaled_down_geobox(src_geobox, ovr_scale)[scaled_down_roi(roi, ovr_scale)]
else:
ovr_geobox = src_geobox[roi]
ovr_im = src.read(band,
window=w_[roi],
out_shape=ovr_geobox.shape)
dst = np.empty(ovr_im.shape[:-2] + dst_geobox.shape, dtype=ovr_im.dtype)
reproject(ovr_im, dst,
src_transform=ovr_geobox.transform,
src_crs=src.crs,
src_nodata=src_nodata,
dst_crs=str(dst_geobox.crs),
dst_transform=dst_geobox.transform,
dst_nodata=dst_nodata,
resampling=resampling)
return dst