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"""
Rasterio exposes GDAL's resampling/decimation on I/O. These are the tests
that it does this correctly.
"""
import numpy as np
import pytest
import rasterio
from rasterio.enums import Resampling
from rasterio.errors import ResamplingAlgorithmError
from rasterio.windows import Window
# Rasterio's test dataset is 718 rows by 791 columns.
def test_read_out_shape_resample_down():
with rasterio.open('tests/data/RGB.byte.tif') as s:
out = np.zeros((8, 8), dtype=rasterio.ubyte)
data = s.read(1, out=out)
expected = np.array([
[ 0, 0, 20, 15, 0, 0, 0, 0],
[ 0, 6, 193, 9, 255, 127, 23, 39],
[ 0, 7, 27, 255, 193, 14, 28, 34],
[ 0, 31, 29, 44, 14, 22, 43, 0],
[ 0, 9, 69, 49, 17, 22, 255, 0],
[ 11, 7, 13, 25, 13, 29, 33, 0],
[ 8, 10, 88, 27, 20, 33, 25, 0],
[ 0, 0, 0, 0, 98, 23, 0, 0]], dtype=np.uint8)
assert (data == expected).all() # all True.
def test_read_out_shape_resample_up():
# Instead of testing array items, test statistics. Upsampling by an even
# constant factor shouldn't change the mean.
with rasterio.open('tests/data/RGB.byte.tif') as s:
out = np.zeros((7180, 7910), dtype=rasterio.ubyte)
data = s.read(1, out=out, masked=True)
assert data.shape == (7180, 7910)
assert data.mean() == s.read(1, masked=True).mean()
# TODO: justify or remove this test.
def test_read_downsample_alpha():
with rasterio.Env(GTIFF_IMPLICIT_JPEG_OVR=False):
with rasterio.open('tests/data/alpha.tif') as src:
out = np.zeros((100, 100), dtype=rasterio.ubyte)
assert src.width == 1223
assert src.height == 1223
assert src.count == 4
assert src.read(1, out=out, masked=False).shape == out.shape
# attempt decimated read of alpha band
src.read(4, out=out, masked=False)
def test_resample_alg_effect_1():
"""default (nearest) and cubic produce different results"""
with rasterio.open('tests/data/RGB.byte.tif') as s:
# Existence of overviews can upset our expectations, so we
# guard against that here.
assert not any([s.overviews(bidx) for bidx in s.indexes])
out_shape = (s.height // 2, s.width // 2)
nearest = s.read(1, out_shape=out_shape)
cubic = s.read(1, out_shape=out_shape, resampling=Resampling.cubic)
assert np.any(nearest != cubic)
def test_resample_alg_effect_2():
"""Average and bilinear produce different results"""
with rasterio.open('tests/data/RGB.byte.tif') as s:
# Existence of overviews can upset our expectations, so we
# guard against that here.
assert not any([s.overviews(bidx) for bidx in s.indexes])
out_shape = (s.height // 2, s.width // 2)
avg = s.read(1, out_shape=out_shape, resampling=Resampling.average)
bilin = s.read(1, out_shape=out_shape, resampling=Resampling.bilinear)
assert np.any(avg != bilin)
def test_float_window():
"""floating point windows work"""
with rasterio.open('tests/data/RGB.byte.tif') as s:
out_shape = (401, 401)
window = Window(300.5, 300.5, 200.5, 200.5)
s.read(1, window=window, out_shape=out_shape)
def test_resampling_alg_error():
"""Get an exception instead of a crash when using warp-only algs for read or write, see issue #1930"""
with pytest.raises(ResamplingAlgorithmError):
with rasterio.open("tests/data/RGB.byte.tif") as src:
src.read(1, out_shape=(1, 10, 10), resampling=Resampling.max)
def test_resampling_rms():
"""Test Resampling.rms method"""
with rasterio.open('tests/data/float.tif') as s:
out_shape = (2, 2)
rms = s.read(1, out_shape=out_shape, resampling=Resampling.rms)
expected = np.array([
[1.35266399, 0.95388681],
[0.29308701, 1.54074657]], dtype=np.float32)
assert np.allclose(rms, expected)
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