def sub_n_reproj(input_mat, kwargs_sub, sub_window, output_crs):
# Get the georeference and bounding parameters of subset image
kwargs_sub.update({
'height': sub_window.height,
'width': sub_window.width,
'transform': rasterio.windows.transform(sub_window, kwargs_sub['transform'])})
# Generate subset rasterio dataset
input_ds_subset = MemoryFile().open(**kwargs_sub)
input_mat = np.expand_dims(input_mat, axis=0)
input_ds_subset.write(input_mat)
# Reproject a dataset
transform_reproj, width_reproj, height_reproj = \
calculate_default_transform(input_ds_subset.crs, output_crs,
input_ds_subset.width, input_ds_subset.height, *input_ds_subset.bounds)
kwargs_reproj = input_ds_subset.meta.copy()
kwargs_reproj.update({
'crs': output_crs,
'transform': transform_reproj,
'width': width_reproj,
'height': height_reproj
})
output_ds = MemoryFile().open(**kwargs_reproj)
reproject(source=rasterio.band(input_ds_subset, 1), destination=rasterio.band(output_ds, 1),
src_transform=input_ds_subset.transform, src_crs=input_ds_subset.crs,
dst_transform=transform_reproj, dst_crs=output_crs, resampling=Resampling.nearest)
return output_ds
def read_tif_channels(tif, channel_index):
if not isinstance(channel_index, list):
channel_index = list(channel_index)
if channel_index[
0] == 0: # rasterio indexes channels starting from 1 not 0...
channel_index = list(np.array(channel_index) + 1)
profile = tif.profile
profile.update({'count': len(channel_index)})
memory_file = MemoryFile().open(**profile)
memory_file.write(tif.read(channel_index))
return memory_file
def mosaic(directory):
file_paths = glob(directory + "/*.tif")
tif_list = []
for path in file_paths:
tif = rasterio.open(path)
tif_list.append(resample_tif(tif, tif.transform[0] / rs.ground_truth.data.resolution))
mosaic_tif, mosaic_transform = merge(tif_list)
profile = tif_list[0].profile
profile.update({'width': mosaic_tif.shape[-1],
'height': mosaic_tif.shape[-2],
'transform': mosaic_transform})
memory_file = MemoryFile().open(**profile)
memory_file.write(mosaic_tif)
return memory_file
def resample_tif(tif, scale_factor, mode=Resampling.bilinear):
data = tif.read(out_shape=(tif.count, int(tif.height * scale_factor),
int(tif.width * scale_factor)),
resampling=mode)
# scale image transform
transform = tif.transform * tif.transform.scale(
(tif.width / data.shape[-1]), (tif.height / data.shape[-2]))
profile = tif.profile
profile.update({
'width': int(tif.width * scale_factor),
'height': int(tif.height * scale_factor),
'transform': transform
})
memory_file = MemoryFile().open(**profile)
memory_file.write(data)
return memory_file
kwargs = smap_sm_1km_ds.meta.copy()
kwargs.update({
'height':
window.height,
'width':
window.width,
'count':
1,
'transform':
rasterio.windows.transform(window, smap_sm_1km_ds.transform)
})
smap_sm_1km_subset = MemoryFile().open(**kwargs)
mat_sub = smap_sm_1km_ds.read(1, window=window)
mat_sub = np.expand_dims(mat_sub, axis=0)
smap_sm_1km_subset.write(mat_sub)
# Define the projection as WGS84
# transform = from_origin(shp_dan_extent[0], shp_dan_extent[3], cellsize_1km, cellsize_1km)
# reproj_data = MemoryFile().open('GTiff', width=smap_sm_1km.shape[1], height=smap_sm_1km.shape[0],
# dtype=str(smap_sm_1km.dtype), count=1, crs='EPSG:4326', transform=transform)
# reproj_data.write(smap_sm_1km, 1)
# reproj_out = rasterio.open('/Users/binfang/Downloads/raster.tif', 'w', 'GTiff',
# width=(lon_1km_dan[-1] - lon_1km_dan[0])//cellsize_1km,
# height=(lat_1km_dan[0] - lat_1km_dan[-1])//cellsize_1km,
# dtype=str(smap_sm_1km.dtype), count=1, crs='EPSG:4326', transform=transform)
# reproj_out.write(smap_sm_1km, 1)
# reproj_out.close()