def nodata_transform(min_val, in_path, out_path):
"""Convert values less than min_val to 0/NODATA.
This script was developed to deal with imagery over Belize City which contains values
that appear to be NODATA values on the periphery of the image, but are actually very
small values < 7. This script converts those small values to zeros which Raster
Vision knows to ignore when making predictions.
"""
min_val = float(min_val)
chip_size = 1000
with rasterio.open(in_path, 'r') as in_data:
with rasterio.open(out_path, 'w', **in_data.profile) as out_data:
extent = Box(0, 0, in_data.height, in_data.width)
windows = extent.get_windows(chip_size, chip_size)
for w in windows:
# Avoid going off edge of array.
if w.ymax > in_data.height:
w.ymax = in_data.height
if w.xmax > in_data.width:
w.xmax = in_data.width
print('.', end='', flush=True)
w = w.rasterio_format()
im = in_data.read(window=w)
nodata_mask = np.all(im < min_val, axis=0)
for b in range(im.shape[0]):
band = im[b, :, :]
band[nodata_mask] = 0
out_data.write_band(b + 1, band, window=w)
def save_image_crop(image_uri,
image_crop_uri,
label_uri=None,
label_crop_uri=None,
size=600,
min_features=10,
vector_labels=True,
class_config=None):
"""Save a crop of an image to use for testing.
If label_uri is set, the crop needs to cover >= min_features.
Args:
image_uri: URI of original image
image_crop_uri: URI of cropped image to save
label_uri: optional URI of label file
label_crop_uri: optional URI of cropped labels to save
size: height and width of crop
Raises:
ValueError if cannot find a crop satisfying min_features constraint.
"""
if not file_exists(image_crop_uri):
print('Saving test crop to {}...'.format(image_crop_uri))
old_environ = os.environ.copy()
try:
request_payer = S3FileSystem.get_request_payer()
if request_payer == 'requester':
os.environ['AWS_REQUEST_PAYER'] = request_payer
im_dataset = rasterio.open(image_uri)
h, w = im_dataset.height, im_dataset.width
extent = Box(0, 0, h, w)
windows = extent.get_windows(size, size)
if label_uri and vector_labels:
crs_transformer = RasterioCRSTransformer.from_dataset(
im_dataset)
geojson_vs_config = GeoJSONVectorSourceConfig(
uri=label_uri, default_class_id=0, ignore_crs_field=True)
vs = geojson_vs_config.build(class_config, crs_transformer)
geojson = vs.get_geojson()
geoms = []
for f in geojson['features']:
g = shape(f['geometry'])
geoms.append(g)
tree = STRtree(geoms)
def p2m(x, y, z=None):
return crs_transformer.pixel_to_map((x, y))
for w in windows:
use_window = True
if label_uri and vector_labels:
w_polys = tree.query(w.to_shapely())
use_window = len(w_polys) >= min_features
if use_window and label_crop_uri is not None:
print('Saving test crop labels to {}...'.format(
label_crop_uri))
label_crop_features = [
mapping(transform(p2m, wp)) for wp in w_polys
]
label_crop_json = {
'type':
'FeatureCollection',
'features': [{
'geometry': f
} for f in label_crop_features]
}
json_to_file(label_crop_json, label_crop_uri)
if use_window:
crop_image(image_uri, w, image_crop_uri)
if not vector_labels and label_uri and label_crop_uri:
crop_image(label_uri, w, label_crop_uri)
break
if not use_window:
raise ValueError('Could not find a good crop.')
finally:
os.environ.clear()
os.environ.update(old_environ)
def save_image_crop(image_uri,
crop_uri,
label_uri=None,
size=600,
min_features=10):
"""Save a crop of an image to use for testing.
If label_uri is set, the crop needs to cover >= min_features.
Args:
image_uri: URI of original image
crop_uri: URI of cropped image to save
label_uri: optional URI of GeoJSON file
size: height and width of crop
Raises:
ValueError if cannot find a crop satisfying min_features constraint.
"""
if not file_exists(crop_uri):
print('Saving test crop to {}...'.format(crop_uri))
old_environ = os.environ.copy()
try:
request_payer = S3FileSystem.get_request_payer()
if request_payer == 'requester':
os.environ['AWS_REQUEST_PAYER'] = request_payer
im_dataset = rasterio.open(image_uri)
h, w = im_dataset.height, im_dataset.width
extent = Box(0, 0, h, w)
windows = extent.get_windows(size, size)
if label_uri is not None:
crs_transformer = RasterioCRSTransformer.from_dataset(
im_dataset)
vs = GeoJSONVectorSource(label_uri, crs_transformer)
geojson = vs.get_geojson()
geoms = []
for f in geojson['features']:
g = shape(f['geometry'])
geoms.append(g)
tree = STRtree(geoms)
for w in windows:
use_window = True
if label_uri is not None:
w_polys = tree.query(w.to_shapely())
use_window = len(w_polys) >= min_features
if use_window:
w = w.rasterio_format()
im = im_dataset.read(window=w)
if np.mean(np.sum(im, axis=2).ravel() == 0) < 0.9:
with tempfile.TemporaryDirectory() as tmp_dir:
crop_path = get_local_path(crop_uri, tmp_dir)
make_dir(crop_path, use_dirname=True)
meta = im_dataset.meta
meta['width'], meta['height'] = size, size
meta['transform'] = rasterio.windows.transform(
w, im_dataset.transform)
with rasterio.open(crop_path, 'w', **meta) as dst:
dst.colorinterp = im_dataset.colorinterp
dst.write(im)
upload_or_copy(crop_path, crop_uri)
break
if not use_window:
raise ValueError('Could not find a good crop.')
finally:
os.environ.clear()
os.environ.update(old_environ)
