def test_generate_epipolar_grids():
"""
Test generate_epipolar_grids method
"""
img1 = absolute_data_path("input/phr_ventoux/left_image.tif")
img2 = absolute_data_path("input/phr_ventoux/right_image.tif")
dem = absolute_data_path("input/phr_ventoux/srtm")
left_grid, right_grid, size_x, size_y, baseline = preprocessing.generate_epipolar_grids(
img1, img2, dem)
assert size_x == 612
assert size_y == 612
assert baseline == 0.7039416432380676
# Uncomment to update baseline
# left_grid.to_netcdf(absolute_data_path("ref_output/left_grid.nc"))
left_grid_ref = xr.open_dataset(
absolute_data_path("ref_output/left_grid.nc"))
assert_same_datasets(left_grid, left_grid_ref)
# Uncomment to update baseline
# right_grid.to_netcdf(absolute_data_path("ref_output/right_grid.nc"))
right_grid_ref = xr.open_dataset(
absolute_data_path("ref_output/right_grid.nc"))
assert_same_datasets(right_grid, right_grid_ref)
def test_images_pair_to_3d_points(images_and_grids_conf, color1_conf, no_data_conf, disparities_conf,
epipolar_origins_spacings_conf, epipolar_sizes_conf):
"""
Test images_pair_to_3d_points on ventoux dataset (epipolar geometry)
with Pandora
"""
# With nodata and color
configuration = images_and_grids_conf
configuration["input"].update(color1_conf["input"])
configuration["input"].update(no_data_conf["input"])
configuration["preprocessing"]["output"].update(epipolar_sizes_conf["preprocessing"]["output"])
configuration["preprocessing"]["output"].update(epipolar_origins_spacings_conf["preprocessing"]["output"])
configuration["preprocessing"]["output"].update(disparities_conf["preprocessing"]["output"])
region = [420, 200, 530, 320]
# Pandora configuration
corr_cfg = create_corr_conf()
cloud, color = stereo.images_pair_to_3d_points(configuration,
region,
corr_cfg,
disp_min=-13,
disp_max=14)
# Uncomment to update baseline
#cloud['ref'].to_netcdf(absolute_data_path("ref_output/cloud1_ref_pandora.nc"))
ref = xr.open_dataset(absolute_data_path("ref_output/cloud1_ref_pandora.nc"))
assert_same_datasets(cloud['ref'],ref,atol=1.e-3)
def test_simple_rasterization_dataset_2():
cloud = xr.open_dataset(
absolute_data_path("input/intermediate_results/cloud1_ref.nc"))
color = xr.open_dataset(
absolute_data_path("input/intermediate_results/data1_ref_clr.nc"))
utm = projection.points_cloud_conversion_dataset(cloud, 32630)
xstart = None
ystart = None
xsize = None
ysize = None
resolution = 0.5
raster = rasterization.simple_rasterization_dataset([utm], resolution,
32630, [color], xstart,
ystart, xsize, ysize,
0.3, 3)
# Uncomment to update references
# raster.to_netcdf(
# absolute_data_path('ref_output/rasterization_res_ref_2.nc'),
# )
raster_ref = xr.open_dataset(
absolute_data_path("ref_output/rasterization_res_ref_2.nc"))
assert_same_datasets(raster, raster_ref, atol=1.e-10, rtol=1.e-10)
def test_resample_image():
"""
Test resample image method
"""
region = [387, 180, 564, 340]
img = absolute_data_path('input/phr_ventoux/left_image.tif')
nodata = 0
grid = absolute_data_path('input/stereo_input/left_epipolar_grid.tif')
epipolar_size_x = 612
epipolar_size_y = 612
test_dataset = stereo.resample_image(
img, grid, [
epipolar_size_x, epipolar_size_y], region=region, nodata=nodata)
# For convenience we use same reference as test_epipolar_rectify_images_1
ref_dataset = xr.open_dataset(absolute_data_path(
"ref_output/data1_ref_left.nc"))
# We need to remove attributes that are not generated by resample_image
# method
ref_dataset.attrs.pop(cst.ROI, None)
ref_dataset.attrs.pop(cst.EPI_MARGINS, None)
ref_dataset.attrs.pop(cst.EPI_DISP_MIN, None)
ref_dataset.attrs.pop(cst.EPI_DISP_MAX, None)
ref_dataset.attrs['region'] = ref_dataset.attrs[cst.ROI_WITH_MARGINS]
ref_dataset.attrs.pop(cst.ROI_WITH_MARGINS, None)
assert_same_datasets(test_dataset, ref_dataset)
def test_compute_disparity_1_msk_ref():
"""
Test compute_disparity on ventoux dataset with pandora
"""
left_input = xr.open_dataset(absolute_data_path("input/intermediate_results/data1_ref_left_masked.nc"))
right_input = xr.open_dataset(absolute_data_path("input/intermediate_results/data1_ref_right.nc"))
# Pandora configuration
corr_cfg = create_corr_conf()
disp_min = -13
disp_max = 14
output = stereo.compute_disparity(left_input,
right_input,
corr_cfg,
disp_min,
disp_max,
verbose = True)
assert output['ref']['disp'].shape == (120, 110)
assert output['ref']['msk'].shape == (120, 110)
np.testing.assert_allclose(output['ref'].attrs["roi"],
np.array([420, 200, 530, 320]))
# Uncomment to update baseline
#output['ref'].to_netcdf(absolute_data_path("ref_output/disp1_ref_pandora_msk_ref.nc"))
#output['sec'].to_netcdf(absolute_data_path("ref_output/disp1_sec_pandora_msk_ref.nc"))
ref = xr.open_dataset(absolute_data_path("ref_output/disp1_ref_pandora_msk_ref.nc"))
assert_same_datasets(output['ref'],ref,atol=5.e-6)
sec = xr.open_dataset(absolute_data_path("ref_output/disp1_sec_pandora_msk_ref.nc"))
assert_same_datasets(output['sec'], sec, atol=5.e-6)
def test_simple_rasterization_dataset_2():
"""
Test simple rasterization dataset from test cloud cloud1_ref_epsg_32630.nc
Configuration 2 : no xstart, ystart, xsize, ysize values
"""
cloud = xr.open_dataset(
absolute_data_path(
"input/rasterization_input/cloud1_ref_epsg_32630.nc"))
color = xr.open_dataset(
absolute_data_path("input/intermediate_results/data1_ref_clr.nc"))
xstart = None
ystart = None
xsize = None
ysize = None
resolution = 0.5
raster = rasterization.simple_rasterization_dataset([cloud], resolution,
32630, [color], xstart,
ystart, xsize, ysize,
0.3, 3)
# Uncomment to update references
# raster.to_netcdf(
# absolute_data_path('ref_output/rasterization_res_ref_2.nc'),
# )
raster_ref = xr.open_dataset(
absolute_data_path("ref_output/rasterization_res_ref_2.nc"))
assert_same_datasets(raster, raster_ref, atol=1.e-10, rtol=1.e-10)
def test_resample_image():
"""
Test resample image method
"""
region = [387, 180, 564, 340]
img = absolute_data_path('input/phr_ventoux/left_image.tif')
nodata = 0
grid = absolute_data_path('input/stereo_input/left_epipolar_grid.tif')
epipolar_size_x = 612
epipolar_size_y = 612
ds = stereo.resample_image(
img, grid, [
epipolar_size_x, epipolar_size_y], region=region, nodata=nodata)
# For convenience we use same reference as test_epipolar_rectify_images_1
ref_ds = xr.open_dataset(absolute_data_path(
"ref_output/data1_ref_left.nc"))
# We need to remove attributes that are not generated by resample_image
# method
ref_ds.attrs.pop('roi', None)
ref_ds.attrs.pop('margins', None)
ref_ds.attrs.pop('disp_min', None)
ref_ds.attrs.pop('disp_max', None)
ref_ds.attrs['region'] = ref_ds.attrs['roi_with_margins']
ref_ds.attrs.pop('roi_with_margins', None)
assert_same_datasets(ds, ref_ds)
def test_simple_rasterization_single():
"""
Test simple rasterization from test cloud ref_single_cloud_in_df.nc
"""
resolution = 0.5
cloud_xr = xr.open_dataset(
absolute_data_path(
'input/rasterization_input/ref_single_cloud_in_df.nc'))
cloud_df = cloud_xr.to_dataframe()
xstart, ystart, xsize, ysize = \
rasterization.compute_xy_starts_and_sizes(resolution, cloud_df)
raster = rasterization.\
rasterize(
cloud_df, resolution, 32630, xstart, ystart, xsize, ysize, 0.3, 3,
hgt_no_data=np.nan, color_no_data=np.nan)
# Uncomment to update references
# raster.to_netcdf(
# absolute_data_path('ref_output/ref_simple_rasterization.nc'),
# )
ref_rasterized = xr.open_dataset(
absolute_data_path("ref_output/ref_simple_rasterization.nc"))
assert_same_datasets(raster, ref_rasterized, atol=1.e-10, rtol=1.e-10)
def test_generate_epipolar_grids_default_alt():
"""
Test generate_epipolar_grids method
"""
img1 = absolute_data_path("input/phr_ventoux/left_image.tif")
img2 = absolute_data_path("input/phr_ventoux/right_image.tif")
dem = None
default_alt = 500
left_grid, right_grid, size_x, size_y, baseline = \
generate_epipolar_grids(
img1, img2, dem, default_alt)
assert size_x == 612
assert size_y == 612
assert baseline == 0.7039446234703064
# Uncomment to update baseline
# left_grid.to_netcdf(absolute_data_path(
# "ref_output/left_grid_default_alt.nc"))
left_grid_ref = xr.open_dataset(
absolute_data_path("ref_output/left_grid_default_alt.nc"))
assert_same_datasets(left_grid, left_grid_ref)
# Uncomment to update baseline
# right_grid.to_netcdf(absolute_data_path(
# "ref_output/right_grid_default_alt.nc"))
right_grid_ref = xr.open_dataset(
absolute_data_path("ref_output/right_grid_default_alt.nc"))
assert_same_datasets(right_grid, right_grid_ref)
def test_compute_disparity_1_msk_sec(
images_and_grids_conf): #pylint: disable=redefined-outer-name
"""
Test compute_disparity on ventoux dataset with pandora
"""
left_input = xr.open_dataset(
absolute_data_path(
"input/intermediate_results/data1_ref_left.nc"))
right_input = xr.open_dataset(
absolute_data_path(
"input/intermediate_results/data1_ref_right_masked.nc"))
conf = deepcopy(images_and_grids_conf)
conf['input']['mask2_classes'] = absolute_data_path(
"input/intermediate_results/data1_ref_right_mask_classes.json")
# Pandora configuration
corr_cfg = create_corr_conf()
disp_min = -13
disp_max = 14
output = stereo.compute_disparity(left_input,
right_input,
conf,
corr_cfg,
disp_min,
disp_max,
verbose=True)
assert output[cst.STEREO_REF][cst.DISP_MAP].shape == (120, 110)
assert output[cst.STEREO_REF][cst.DISP_MSK].shape == (120, 110)
assert output[cst.STEREO_SEC][cst.DISP_MAP].shape == (160, 177)
assert output[cst.STEREO_SEC][cst.DISP_MSK].shape == (160, 177)
np.testing.assert_allclose(
output[cst.STEREO_REF].attrs[cst.ROI],
np.array([420, 200, 530, 320]))
np.testing.assert_allclose(
output[cst.STEREO_SEC].attrs[cst.ROI],
np.array([420, 200, 530, 320]))
np.testing.assert_allclose(
output[cst.STEREO_SEC].attrs[cst.ROI_WITH_MARGINS],
np.array([387, 180, 564, 340]))
# Uncomment to update baseline
# output[cst.STEREO_REF].to_netcdf(absolute_data_path(
# "ref_output/disp1_ref_pandora_msk_sec.nc"))
# output[cst.STEREO_SEC].to_netcdf(absolute_data_path(
# "ref_output/disp1_sec_pandora_msk_sec.nc"))
ref = xr.open_dataset(
absolute_data_path(
"ref_output/disp1_ref_pandora_msk_sec.nc"))
assert_same_datasets(output[cst.STEREO_REF],ref,atol=5.e-6)
sec = xr.open_dataset(
absolute_data_path(
"ref_output/disp1_sec_pandora_msk_sec.nc"))
assert_same_datasets(output[cst.STEREO_SEC], sec, atol=5.e-6)
def test_compute_disparity_3(
images_and_grids_conf): #pylint: disable=redefined-outer-name
"""
Test compute_disparity on paca dataset with pandora
"""
left_input = xr.open_dataset(absolute_data_path(
"input/intermediate_results/data3_ref_left.nc"))
right_input = xr.open_dataset(absolute_data_path(
"input/intermediate_results/data3_ref_right.nc"))
# Pandora configuration
corr_cfg = create_corr_conf()
disp_min = -43
disp_max = 41
output = stereo.compute_disparity(left_input,
right_input,
images_and_grids_conf,
corr_cfg,
disp_min,
disp_max)
assert output[cst.STEREO_REF][cst.DISP_MAP].shape == (90, 90)
assert output[cst.STEREO_REF][cst.DISP_MSK].shape == (90, 90)
assert output[cst.STEREO_SEC][cst.DISP_MAP].shape == (170, 254)
assert output[cst.STEREO_SEC][cst.DISP_MSK].shape == (170, 254)
np.testing.assert_allclose(
output[cst.STEREO_REF].attrs[cst.ROI],
np.array([16500, 23160, 16590, 23250]))
np.testing.assert_allclose(
output[cst.STEREO_SEC].attrs[cst.ROI],
np.array([16500, 23160, 16590, 23250]))
np.testing.assert_allclose(
output[cst.STEREO_SEC].attrs[cst.ROI_WITH_MARGINS],
np.array([16417, 23120, 16671, 23290]))
# Uncomment to update baseline
# output[cst.STEREO_REF].to_netcdf(absolute_data_path(
# "ref_output/disp3_ref_pandora.nc"))
# output[cst.STEREO_SEC].to_netcdf(absolute_data_path(
# "ref_output/disp3_sec_pandora.nc"))
ref = xr.open_dataset(absolute_data_path(
"ref_output/disp3_ref_pandora.nc"))
assert_same_datasets(output[cst.STEREO_REF], ref, atol=5.e-6)
sec = xr.open_dataset(absolute_data_path(
"ref_output/disp3_sec_pandora.nc"))
assert_same_datasets(output[cst.STEREO_SEC], sec, atol=5.e-6)
def test_triangulation_1(images_and_grids_conf):
"""
Test triangulation ventoux dataset
"""
input = xr.open_dataset(absolute_data_path(
"input/intermediate_results/disp1_ref.nc"))
output = stereo.triangulate(images_and_grids_conf, input, None)
assert output['ref']['x'].shape == (120, 110)
# Uncomment to update baseline
#output['ref'].to_netcdf(absolute_data_path("ref_output/triangulation1_ref.nc"))
ref = xr.open_dataset(absolute_data_path(
"ref_output/triangulation1_ref.nc"))
assert_same_datasets(output['ref'], ref, atol=1.e-3)
def test_triangulation_1(
images_and_grids_conf): #pylint: disable=redefined-outer-name
"""
Test triangulation ventoux dataset
"""
disp1_ref = xr.open_dataset(absolute_data_path(
"input/intermediate_results/disp1_ref.nc"))
point_cloud_dict = stereo.triangulate(
images_and_grids_conf, disp1_ref, None)
assert point_cloud_dict[cst.STEREO_REF][cst.X].shape == (120, 110)
# Uncomment to update baseline
# output[cst.STEREO_REF].to_netcdf(
# absolute_data_path("ref_output/triangulation1_ref.nc"))
ref = xr.open_dataset(absolute_data_path(
"ref_output/triangulation1_ref.nc"))
assert_same_datasets(point_cloud_dict[cst.STEREO_REF], ref, atol=1.e-3)
def test_compute_disparity_3():
"""
Test compute_disparity on paca dataset with pandora
"""
left_input = xr.open_dataset(absolute_data_path(
"input/intermediate_results/data3_ref_left.nc"))
right_input = xr.open_dataset(absolute_data_path(
"input/intermediate_results/data3_ref_right.nc"))
# Pandora configuration
corr_cfg = create_corr_conf()
disp_min = -43
disp_max = 41
output = stereo.compute_disparity(left_input,
right_input,
corr_cfg,
disp_min,
disp_max)
assert output['ref']['disp'].shape == (90, 90)
assert output['ref']['msk'].shape == (90, 90)
assert output['sec']['disp'].shape == (170, 254)
assert output['sec']['msk'].shape == (170, 254)
np.testing.assert_allclose(output['ref'].attrs["roi"],
np.array([16500, 23160, 16590, 23250]))
np.testing.assert_allclose(output['sec'].attrs["roi"],
np.array([16500, 23160, 16590, 23250]))
np.testing.assert_allclose(output['sec'].attrs["roi_with_margins"],
np.array([16417, 23120, 16671, 23290]))
# Uncomment to update baseline
#output['ref'].to_netcdf(absolute_data_path("ref_output/disp3_ref_pandora.nc"))
#output['sec'].to_netcdf(absolute_data_path("ref_output/disp3_sec_pandora.nc"))
ref = xr.open_dataset(absolute_data_path(
"ref_output/disp3_ref_pandora.nc"))
assert_same_datasets(output['ref'], ref, atol=5.e-6)
sec = xr.open_dataset(absolute_data_path(
"ref_output/disp3_sec_pandora.nc"))
assert_same_datasets(output['sec'], sec, atol=5.e-6)
def test_read_lowres_dem():
"""
Test read_lowres_dem function
"""
dem = absolute_data_path("input/phr_ventoux/srtm")
startx = 5.193458
starty = 44.206671
sizex = 100
sizey = 100
srtm_ds = preprocessing.read_lowres_dem(dem, startx, starty, sizex, sizey)
# Uncomment to update baseline
#srtm_ds.to_netcdf(absolute_data_path("ref_output/srtm_xt.nc"))
srtm_ds_ref = xr.open_dataset(
absolute_data_path("ref_output/srtm_xt.nc"))
assert_same_datasets(srtm_ds, srtm_ds_ref)
print(srtm_ds)
def test_update_disp_to_0_no_tags_in_jsons(
ref_ds, sec_ds, ref_disp, sec_disp): #pylint: disable=redefined-outer-name
"""Tests update disp to 0 with an empty dict"""
# disp dictionary
disp = {
cst.STEREO_REF: ref_disp,
cst.STEREO_SEC: sec_disp
}
# test
disp_no_tags_in_json = deepcopy(disp)
matching_regularisation.update_disp_to_0(
disp_no_tags_in_json, ref_ds, sec_ds,
absolute_data_path(
"input/matching_regularisation_input/mask_no_set_to_ref_alt_classes.json"),
absolute_data_path(
"input/matching_regularisation_input/mask_no_set_to_ref_alt_classes.json"))
assert_same_datasets(disp_no_tags_in_json[cst.STEREO_REF],
disp[cst.STEREO_REF])
assert_same_datasets(disp_no_tags_in_json[cst.STEREO_SEC],
disp[cst.STEREO_SEC])
def test_simple_rasterization_multiple_datasets():
"""
Test simple_rasterization_dataset with a list of datasets
"""
cloud = xr.open_dataset(
absolute_data_path("input/intermediate_results/cloud1_ref.nc"))
color = xr.open_dataset(
absolute_data_path("input/intermediate_results/data1_ref_clr.nc"))
utm = projection.points_cloud_conversion_dataset(cloud, 32630)
utm1 = utm.isel(row=range(0, 60))
utm2 = utm.isel(row=range(60, 120))
color1 = color.isel(row=range(0, 60))
color2 = color.isel(row=range(60, 120))
xstart = 1154790
ystart = 4927552
xsize = 114
ysize = 112
resolution = 0.5
raster = rasterization.simple_rasterization_dataset([utm1, utm2],
resolution, 32630,
[color1, color2],
xstart, ystart, xsize,
ysize, 0.3, 3)
# Uncomment to update reference
# raster.to_netcdf(
# absolute_data_path('ref_output/rasterization_multiple_res_ref.nc'),
# )
raster_ref = xr.open_dataset(
absolute_data_path("ref_output/rasterization_multiple_res_ref.nc"))
assert_same_datasets(raster, raster_ref, atol=1.e-10, rtol=1.e-10)
def test_epipolar_rectify_images_1(
images_and_grids_conf, color1_conf, #pylint: disable=redefined-outer-name
epipolar_sizes_conf, #pylint: disable=redefined-outer-name
epipolar_origins_spacings_conf, #pylint: disable=redefined-outer-name
no_data_conf): #pylint: disable=redefined-outer-name
"""
Test epipolar_rectify_image on ventoux dataset (epipolar geometry)
with nodata and color
"""
configuration = images_and_grids_conf
configuration["input"].update(color1_conf["input"])
configuration["input"].update(no_data_conf["input"])
configuration["preprocessing"]["output"]\
.update(epipolar_sizes_conf["preprocessing"]["output"])
configuration["preprocessing"]["output"]\
.update(epipolar_origins_spacings_conf["preprocessing"]["output"])
region = [420, 200, 530, 320]
margin = xr.DataArray(np.array([[33, 20, 34, 20],
[33, 20, 34, 20]], dtype=int),
coords=[['ref_margin', 'sec_margin'],
['left', 'up', 'right', 'down']],
dims=['image', 'corner'])
margin.name = cst.EPI_MARGINS
margin.attrs[cst.EPI_DISP_MIN] = -13
margin.attrs[cst.EPI_DISP_MAX] = 14
# Rectify images
left, right, clr = stereo.epipolar_rectify_images(configuration,
region,
margin)
print("\nleft dataset: {}".format(left))
print("right dataset: {}".format(right))
print("clr dataset: {}".format(clr))
# Uncomment to update baseline
# left.to_netcdf(absolute_data_path("ref_output/data1_ref_left.nc"))
left_ref = xr.open_dataset(
absolute_data_path("ref_output/data1_ref_left.nc"))
assert_same_datasets(left, left_ref)
# Uncomment to update baseline
# right.to_netcdf(absolute_data_path("ref_output/data1_ref_right.nc"))
right_ref = xr.open_dataset(
absolute_data_path("ref_output/data1_ref_right.nc"))
assert_same_datasets(right, right_ref)
# Uncomment to update baseline
# clr.to_netcdf(absolute_data_path("ref_output/data1_ref_clr.nc"))
clr_ref = xr.open_dataset(
absolute_data_path("ref_output/data1_ref_clr.nc"))
assert_same_datasets(clr, clr_ref)
def test_epipolar_rectify_images_3(images_and_grids_conf, color_pxs_conf, epipolar_sizes_conf,
epipolar_origins_spacings_conf, no_data_conf):
"""
Test epipolar_rectify_image on ventoux dataset (epipolar geometry)
with nodata and color as a p+xs fusion
"""
configuration = images_and_grids_conf
configuration["input"].update(color_pxs_conf["input"])
configuration["input"].update(no_data_conf["input"])
configuration["preprocessing"]["output"].update(epipolar_sizes_conf["preprocessing"]["output"])
configuration["preprocessing"]["output"].update(epipolar_origins_spacings_conf["preprocessing"]["output"])
region = [420, 200, 530, 320]
margin = xr.DataArray(np.array([[33, 20, 34, 20], [33, 20, 34, 20]], dtype=int),
coords=[['ref_margin', 'sec_margin'],
['left', 'up', 'right', 'down']],
dims=['image', 'corner'])
margin.name = 'Margins'
margin.attrs['disp_min'] = -13
margin.attrs['disp_max'] = 14
# Rectify images
left, right, clr = stereo.epipolar_rectify_images(configuration,
region,
margin)
print("\nleft dataset: {}".format(left))
print("right dataset: {}".format(right))
print("clr dataset: {}".format(clr))
left_ref = xr.open_dataset(
absolute_data_path("ref_output/data1_ref_left.nc"))
assert_same_datasets(left, left_ref)
right_ref = xr.open_dataset(
absolute_data_path("ref_output/data1_ref_right.nc"))
assert_same_datasets(right, right_ref)
# Uncomment to update baseline
# clr.to_netcdf(absolute_data_path("ref_output/data3_ref_clr_4bands.nc"))
clr_ref = xr.open_dataset(absolute_data_path(
"ref_output/data3_ref_clr_4bands.nc"))
assert_same_datasets(clr, clr_ref)
def test_add_cloud_filtering_msk():
nb_row = 5
nb_col = 10
rows = np.array(range(nb_row))
cols = np.array(range(nb_col))
ds0 = xr.Dataset({}, coords={'row': rows, 'col': cols})
ds1 = xr.Dataset({}, coords={'row': rows, 'col': cols})
pos_arr = np.array([[1, 2, 0], [2, 2, 1]])
elt_remove = pandas.DataFrame(
pos_arr,
columns=['coord_epi_geom_i', 'coord_epi_geom_j', 'idx_im_epi'])
filtering.add_cloud_filtering_msk([ds0, ds1], elt_remove, 'mask', 255)
mask0 = np.zeros((nb_row, nb_col), dtype=np.uint16)
mask0[1, 2] = 255
mask1 = np.zeros((nb_row, nb_col), dtype=np.uint16)
mask1[2, 2] = 255
ds0_ref = xr.Dataset({'mask': (['row', 'col'], mask0)},
coords={
'row': rows,
'col': cols
})
ds1_ref = xr.Dataset({'mask': (['row', 'col'], mask1)},
coords={
'row': rows,
'col': cols
})
assert_same_datasets(ds0_ref, ds0)
assert_same_datasets(ds1_ref, ds1)
# test exceptions
with pytest.raises(Exception) as e:
np_pos = np.array([[1, 2, 2], [2, 2, 1]])
elt_remove = pandas.DataFrame(
np_pos,
columns=['coord_epi_geom_i', 'coord_epi_geom_j', 'idx_im_epi'])
filtering.add_cloud_filtering_msk([ds0, ds1], elt_remove, 'mask', 255)
assert str(e) == 'Index indicated in the elt_pos_infos pandas.DataFrame is not coherent ' \
'with the clouds list given in input'
with pytest.raises(Exception) as e:
np_pos = np.array([[1, 2, -1], [2, 2, 1]])
elt_remove = pandas.DataFrame(
np_pos,
columns=['coord_epi_geom_i', 'coord_epi_geom_j', 'idx_im_epi'])
filtering.add_cloud_filtering_msk([ds0, ds1], elt_remove, 'mask', 255)
assert str(e) == 'Index indicated in the elt_pos_infos pandas.DataFrame is not coherent ' \
'with the clouds list given in input'
with pytest.raises(Exception) as e:
np_pos = np.array([[11, 2, 0]])
elt_remove = pandas.DataFrame(
np_pos,
columns=['coord_epi_geom_i', 'coord_epi_geom_j', 'idx_im_epi'])
filtering.add_cloud_filtering_msk([ds0, ds1], elt_remove, 'mask', 255)
assert str(
e
) == 'Point at location (11,2) is not accessible in an image of size (5,10)'
