Python features.rasterize方法代码示例

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def burn(tile, features, size):
    """Burn tile with features.

    Args:
      tile: the mercantile tile to burn.
      features: the geojson features to burn.
      size: the size of burned image.

    Returns:
      image: rasterized file of size with features burned.
    """

    # the value you want in the output raster where a shape exists
    burnval = 1
    shapes = ((geometry, burnval) for feature in features for geometry in feature_to_mercator(feature))

    bounds = mercantile.xy_bounds(tile)
    transform = from_bounds(*bounds, size, size)

    return rasterize(shapes, out_shape=(size, size), transform=transform) 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def burn(polys, zoom):
    bounds = find_extrema(polys)
    tilerange = tile_extrema(bounds, zoom)
    afftrans = make_transform(tilerange, zoom)

    burn = features.rasterize(
            ((project_geom(geom['geometry']), 255) for geom in polys),
            out_shape=((tilerange['y']['max'] - tilerange['y']['min'], tilerange['x']['max'] - tilerange['x']['min'])),
            transform=afftrans,
            all_touched=True)

    xys = np.fliplr(np.dstack(np.where(burn))[0])

    xys[:, 0] += tilerange['x']['min']
    xys[:, 1] += tilerange['y']['min']

    return np.append(xys, np.zeros((xys.shape[0], 1), dtype=np.uint8) + zoom, axis=1) 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def _determine_method(lon, lat):
    """ find method to be used -> prefers faster methods"""

    if equally_spaced(lon, lat):
        return "rasterize"

    if _equally_spaced_on_split_lon(lon) and equally_spaced(lat):

        split_point = _find_splitpoint(lon)
        flipped_lon = np.hstack((lon[split_point:], lon[:split_point]))

        if equally_spaced(flipped_lon):
            return "rasterize_flip"
        else:
            return "rasterize_split"

    return "shapely" 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def add_parser(subparser):
    parser = subparser.add_parser(
        "rasterize", help="rasterize features to label masks", formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )

    parser.add_argument("features", type=str, help="path to GeoJSON features file")
    parser.add_argument("tiles", type=str, help="path to .csv tiles file")
    parser.add_argument("out", type=str, help="directory to write converted images")
    parser.add_argument("--dataset", type=str, required=True, help="path to dataset configuration file")
    parser.add_argument("--zoom", type=int, required=True, help="zoom level of tiles")
    parser.add_argument("--size", type=int, default=512, help="size of rasterized image tiles in pixels")

    parser.set_defaults(func=main) 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def _mask_rasterize_no_offset(lon, lat, polygons, numbers, fill=np.NaN, **kwargs):
    """ Rasterize a list of (geometry, fill_value) tuples onto the given coordinates.

        This only works for 1D lat and lon arrays.

        for internal use: does not check valitity of input
    """
    # TODO: use only this function once https://github.com/mapbox/rasterio/issues/1844
    # is resolved

    from rasterio import features

    shapes = zip(polygons, numbers)

    transform = _transform_from_latlon(lon, lat)
    out_shape = (len(lat), len(lon))

    raster = features.rasterize(
        shapes,
        out_shape=out_shape,
        fill=fill,
        transform=transform,
        dtype=np.float,
        **kwargs
    )

    return raster 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def main(args):
    dataset = load_config(args.dataset)

    classes = dataset["common"]["classes"]
    colors = dataset["common"]["colors"]
    assert len(classes) == len(colors), "classes and colors coincide"

    assert len(colors) == 2, "only binary models supported right now"
    bg = colors[0]
    fg = colors[1]

    os.makedirs(args.out, exist_ok=True)

    # We can only rasterize all tiles at a single zoom.
    assert all(tile.z == args.zoom for tile in tiles_from_csv(args.tiles))

    with open(args.features) as f:
        fc = json.load(f)

    # Find all tiles the features cover and make a map object for quick lookup.
    feature_map = collections.defaultdict(list)
    for i, feature in enumerate(tqdm(fc["features"], ascii=True, unit="feature")):

        if feature["geometry"]["type"] != "Polygon":
            continue

        try:
            for tile in burntiles.burn([feature], zoom=args.zoom):
                feature_map[mercantile.Tile(*tile)].append(feature)
        except ValueError as e:
            print("Warning: invalid feature {}, skipping".format(i), file=sys.stderr)
            continue

    # Burn features to tiles and write to a slippy map directory.
    for tile in tqdm(list(tiles_from_csv(args.tiles)), ascii=True, unit="tile"):
        if tile in feature_map:
            out = burn(tile, feature_map[tile], args.size)
        else:
            out = np.zeros(shape=(args.size, args.size), dtype=np.uint8)

        out_dir = os.path.join(args.out, str(tile.z), str(tile.x))
        os.makedirs(out_dir, exist_ok=True)

        out_path = os.path.join(out_dir, "{}.png".format(tile.y))

        if os.path.exists(out_path):
            prev = np.array(Image.open(out_path))
            out = np.maximum(out, prev)

        out = Image.fromarray(out, mode="P")

        palette = make_palette(bg, fg)
        out.putpalette(palette)

        out.save(out_path, optimize=True) 

# 需要导入模块: from rasterio import features [as 别名]
# 或者: from rasterio.features import rasterize [as 别名]
def accuracy(grid_in, guess_in, aoi_in, buffer_amount=0.01):
    """Measure accuracy against a specified grid 'truth' file.

    Parameters
    ----------
    grid_in : str, Path
        Path to vector truth file.
    guess_in : str, Path
        Path to guess output from guess2geom.
    aoi_in : str, Path
        Path to AOI feature.
    buffer_amount : float, optional (default 0.01.)
        Leeway in decimal degrees in calculating equivalence.
        0.01 DD equals approximately 1 mile at the equator.
    """

    if isinstance(aoi_in, gpd.GeoDataFrame):
        aoi = aoi_in
    else:
        aoi = gpd.read_file(aoi_in)

    grid = gpd.read_file(grid_in)
    grid_clipped = clip_line_poly(grid, aoi)
    grid_buff = grid_clipped.buffer(buffer_amount)

    guesses_reader = rasterio.open(guess_in)
    guesses = guesses_reader.read(1)

    grid_for_raster = [(row.geometry) for _, row in grid_clipped.iterrows()]
    grid_raster = rasterize(
        grid_for_raster,
        out_shape=guesses_reader.shape,
        fill=1,
        default_value=0,
        all_touched=True,
        transform=guesses_reader.transform,
    )
    grid_buff_raster = rasterize(
        grid_buff,
        out_shape=guesses_reader.shape,
        fill=1,
        default_value=0,
        all_touched=True,
        transform=guesses_reader.transform,
    )

    grid_raster = flip_arr_values(grid_raster)
    grid_buff_raster = flip_arr_values(grid_buff_raster)

    tp = true_positives(guesses, grid_buff_raster)
    fn = false_negatives(guesses, grid_raster)

    return tp, fn