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)