def test_return_tile():
address = spacenetPath
with rasterio.open(address) as src:
wgs_bounds = utils.get_wgs84_bounds(src)
utm_crs = utils.calculate_UTM_crs(wgs_bounds)
utm_bounds = utils.get_utm_bounds(src, utm_crs)
cells_list = main.calculate_analysis_grid(
utm_bounds,
stride_size_meters=stride_size_meters,
cell_size_meters=cell_size_meters)
random_cell = random.choice(cells_list[0])
ll_x, ll_y, ur_x, ur_y = random_cell
print(random_cell)
tile, mask, window, window_transform = main.tile_utm(
src,
ll_x,
ll_y,
ur_x,
ur_y,
indexes=None,
tilesize=tile_size_pixels,
nodata=None,
alpha=None,
dst_crs=utm_crs)
print(np.shape(tile))
assert np.shape(tile) == (8, tile_size_pixels, tile_size_pixels)
def test_calculate_utm_epsg():
wgs_bounds = utils.get_wgs84_bounds(ADDRESS)
print(wgs_bounds)
utm_crs = utils.calculate_UTM_crs(wgs_bounds)
print(utm_crs)
assert utm_crs == '+proj=utm +zone=13 +north +ellps=WGS84 +datum=WGS84 +units=m +no_defs'
def test_return_vector_tile():
utm_crs = utils.calculate_UTM_crs(
[-115.30170, 36.15604, -115.30170, 36.15604])
gdf = vector_utils.read_vector_file(geojsonPath)
gdf.head()
print(utm_crs)
gdf = vector_utils.transformToUTM(gdf, utm_crs=utm_crs)
utmX, utmY = 658029, 4006947
ll_x = utmX
ur_x = utmX + 500
ll_y = utmY
ur_y = utmY + 500
stride_size_meters = 300
cell_size_meters = 400
tile_size_pixels = 1600
small_gdf = vector_utils.vector_tile_utm(
gdf, tile_bounds=[ll_x, ll_y, ur_x, ur_y])
img = vector_utils.rasterize_gdf(small_gdf,
src_shape=(tile_size_pixels,
tile_size_pixels))
print(img.shape)
assert img.shape[0] == 1600
def get_processing_details(
rasterPath,
smallExample=False,
dstkwargs={
"nodata": 0,
"interleave": "pixel",
"tiled": True,
"blockxsize": 512,
"blockysize": 512,
"compress": "LZW"
}):
with rasterio.open(rasterPath) as src:
# Get Lat, Lon bounds of the Raster (src)
wgs_bounds = utils.get_wgs84_bounds(src)
# Use Lat, Lon location of Image to get UTM Zone/ UTM projection
utm_crs = utils.calculate_UTM_crs(wgs_bounds)
# Calculate Raster bounds in UTM coordinates
utm_bounds = utils.get_utm_bounds(src, utm_crs)
vrt_profile = utils.get_utm_vrt_profile(
src,
crs=utm_crs,
)
dst_profile = vrt_profile
dst_profile.update({
'count': 1,
'dtype': rasterio.uint8,
'driver': "GTiff",
})
# update for CogStandard
dst_profile.update(dstkwargs)
# open s3 Location
rasterBounds = geometry.box(*utm_bounds)
if smallExample:
rasterBounds = geometry.box(*rasterBounds.centroid.buffer(1000).bounds)
return rasterBounds, dst_profile
def test_grid_creation():
address = spacenetPath
with rasterio.open(address) as src:
wgs_bounds = utils.get_wgs84_bounds(src)
utm_EPSG = utils.calculate_UTM_crs(wgs_bounds)
utm_bounds = utils.get_utm_bounds(src, utm_EPSG)
print(utm_bounds)
cells_list_dict = main.calculate_analysis_grid(
utm_bounds,
stride_size_meters=stride_size_meters,
cell_size_meters=cell_size_meters)
print(len(cells_list_dict))
assert len(cells_list_dict[0][0]) == 4
assert ((cells_list_dict[0][0][2] - cells_list_dict[0][0][0]),
(cells_list_dict[0][0][3] -
cells_list_dict[0][0][1])) == (cell_size_meters, cell_size_meters)
assert (cells_list_dict[0][1][1] -
cells_list_dict[0][0][1]) == stride_size_meters
assert len(cells_list_dict[0]) == 2491
os.environ['CURL_CA_BUNDLE'] = '/etc/ssl/certs/ca-certificates.crt'
## give location to SpaceNet 8-Band PanSharpened Geotiff on s3
#spacenetPath = "s3://spacenet-dataset/AOI_2_Vegas/srcData/rasterData/AOI_2_Vegas_MUL-PanSharpen_Cloud.tif"
spacenetPath = "/home/dlindenbaum/datastorage/spacenet_sample/AOI_2_Vegas_MUL-PanSharpen_Cloud.tif"
osm_labels_path = "./tests/fixtures/my-bucket/spacenet_test/las-vegas_nevada_osm_buildings.geojson"
## Prep files for UTM
with rasterio.open(spacenetPath) as src:
# Get Lat, Lon bounds of the Raster (src)
wgs_bounds = utils.get_wgs84_bounds(src)
# Use Lat, Lon location of Image to get UTM Zone/ UTM projection
utm_crs = utils.calculate_UTM_crs(wgs_bounds)
# Calculate Raster bounds in UTM coordinates
utm_bounds = utils.get_utm_bounds(src, utm_crs)
## read vector file
gdf = vector_utils.read_vector_file(osm_labels_path)
gdf.head()
gdf_utm = vector_utils.transformToUTM(gdf, utm_crs=utm_crs)
# open s3 Location
# Each grid starting point will be spaced 400m apart
stride_size_meters = 400
# Each grid cell will be 400m on a side
def test_return_tile_full():
## Set for specific tile in Las Vegas
utm_crs = utils.calculate_UTM_crs(
[-115.30170, 36.15604, -115.30170, 36.15604])
gdf = vector_utils.read_vector_file(geojsonPath)
gdf.head()
gdf = vector_utils.transformToUTM(gdf, utm_crs=utm_crs)
utmX, utmY = 658029, 4006947
ll_x = utmX
ur_x = utmX + 500
ll_y = utmY
ur_y = utmY + 500
stride_size_meters = 300
cell_size_meters = 400
tile_size_pixels = 1600
address = spacenetPath
with rasterio.open(address) as src:
src_profile = src.profile
tile, mask, window, window_transform = main.tile_utm(
src,
ll_x,
ll_y,
ur_x,
ur_y,
indexes=None,
tilesize=tile_size_pixels,
nodata=None,
alpha=None,
dst_crs=utm_crs)
print(np.shape(tile))
assert np.shape(tile) == (8, tile_size_pixels, tile_size_pixels)
small_gdf = vector_utils.vector_tile_utm(
gdf, tile_bounds=[ll_x, ll_y, ur_x, ur_y])
print(small_gdf.shape)
small_gdf.to_file(os.path.join(PREFIX, "testTiff_Label.geojson"),
driver='GeoJSON')
img = vector_utils.rasterize_gdf(
small_gdf,
src_shape=(tile_size_pixels, tile_size_pixels),
src_transform=window_transform,
)
print("Label Count Burn {}:".format(np.sum(img)))
assert img.shape == (tile_size_pixels, tile_size_pixels)
dst_profile = src_profile
dst_profile.update({
'transform': window_transform,
'crs': utm_crs,
'width': tile_size_pixels,
'height': tile_size_pixels,
'count': 1,
'dtype': rasterio.uint8
})
with rasterio.open(os.path.join(PREFIX, "testTiff_Label.tif"), 'w',
**dst_profile) as dst:
dst.write(img, indexes=1)
dst_profile = src_profile
dst_profile.update({
'transform': window_transform,
'crs': utm_crs,
'width': tile_size_pixels,
'height': tile_size_pixels,
'count': 8,
'dtype': rasterio.uint16
})
with rasterio.open(os.path.join(PREFIX, "testTiff_Image.tif"), 'w',
**dst_profile) as dst:
dst.write(tile)
