Blob storage endpoint. Fix ImageryVisualizer (#15)
* Add a method to get the blob storage endpoint used by a SAS URL. * Fix issues in imagery_visualizer to display Landsat8 tiles. * Use better default values for viewing Landsat imagery.
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Siyu Yang
GitHub
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df89c7b7a2
Коммит
4a32fa69a8
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@ -1,7 +1,7 @@
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"""
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Visualize multi-band spectral imagery data that rasterio can load.
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Mainly catering to Landsat, Sentinel2 and SRTM DEM.
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Mainly catering to Landsat, Sentinel-2 and SRTM DEM.
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"""
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import math
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@ -55,7 +55,7 @@ class ImageryVisualizer(object):
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@staticmethod
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def show_single_band(raster: np.ndarray,
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size: Tuple[Numeric, Numeric] = (4, 4),
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size_inches: Tuple[Numeric, Numeric] = (4, 4),
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cmap: Union[mcolors.Colormap, str] = 'gist_yarg',
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normalizer: mcolors.Normalize = normalized_band_normalizer) -> Tuple[Image.Image, BytesIO]:
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"""Visualizes a single band passed in as a numpy array.
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@ -64,6 +64,8 @@ class ImageryVisualizer(object):
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raster: numpy array of imagery values; needs to be 2D after squeezing
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size: matplotlib size in inches (h, w)
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cmap: matplotlib recognized color map str or custom matplotlib colormap object
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normalizer: a matplotlib.colors.Normalize object. Default is one that has min value at -1
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and max at 1.
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Returns:
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(im, buf) - (PIL image of the matplotlib figure, a BytesIO buf containing the matplotlib Figure
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@ -72,7 +74,7 @@ class ImageryVisualizer(object):
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raster = raster.squeeze()
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assert len(raster.shape) == 2, 'Single band should be a 2D array after squeezing.'
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_ = plt.figure(figsize=size)
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_ = plt.figure(figsize=size_inches)
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_ = plt.imshow(raster, cmap=cmap, norm=normalizer)
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buf = BytesIO()
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@ -83,10 +85,10 @@ class ImageryVisualizer(object):
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return im, buf
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@staticmethod
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def _norm_band(bands: np.ndarray,
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band_min: Numeric = 0,
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band_max: Numeric = 7000,
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gamma: Numeric = 1.0) -> np.ndarray:
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def norm_band(bands: np.ndarray,
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band_min: Numeric = 0,
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band_max: Numeric = 7000,
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gamma: Numeric = 1.0) -> np.ndarray:
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"""Clip, normalize by band_min and band_max, and gamma correct a tile. All bands use the
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same band_min, band_max and gamma. If each band should be processed differently, call this function
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with each band and its normalization parameters, and stack afterwards.
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@ -111,7 +113,7 @@ class ImageryVisualizer(object):
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return bands
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@staticmethod
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def show_landsat8_ndvi(tile_reader: rasterio.DatasetReader,
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def get_landsat8_ndvi(tile_reader: rasterio.DatasetReader,
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window: Union[rasterio.windows.Window, Tuple] = None) -> np.ndarray:
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"""Computes the NDVI (Normalized Difference Vegetation Index) over a tile or a section
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on the tile specified by the window, for Landsat 8 tiles.
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@ -180,11 +182,12 @@ class ImageryVisualizer(object):
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`return_array` is False.
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None if do not resize, otherwise a (w, h) tuple in pixel unit.
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Default is (256, 256). (500, 500) looks better in notebooks
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return_array: True will cause this function to return a numpy array of dtype float32;
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return_array: True will cause this function to return a numpy array, with dtype the same as
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the original data;
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False (default) to get a PIL Image object (values scaled to be uint8 values)
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Returns:
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a PIL Image object, resized to `size`, or the (not resized, float32) numpy array
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a PIL Image object, resized to `size`, or the (not resized, original data type) numpy array
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if `return_array` is true. The dims start with height and width, optionally
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with the channel dim at the end if greater than 1.
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@ -202,9 +205,9 @@ class ImageryVisualizer(object):
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window = rasterio.windows.Window(window[0], window[1], window[2], window[3])
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# read as (bands, rows, columns) or (c, h, w)
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bands = tile_reader.read(bands, window=window, boundless=True, fill_value=0) # dtype is float32
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bands = tile_reader.read(bands, window=window, boundless=True, fill_value=0)
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bands = ImageryVisualizer._norm_band(bands, band_min=band_min, band_max=band_max, gamma=gamma)
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bands = ImageryVisualizer.norm_band(bands, band_min=band_min, band_max=band_max, gamma=gamma)
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# need to rearrange to (h, w, channel/bands)
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bands = np.transpose(bands, axes=[1, 2, 0])
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@ -228,17 +231,17 @@ class ImageryVisualizer(object):
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bands: Union[Iterable, int] = landsat8_visible,
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window: Union[rasterio.windows.Window, Tuple] = None,
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band_min: Numeric = 0,
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band_max: Numeric = 7000,
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gamma: Numeric = 1.0,
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band_max: Numeric = 3000,
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gamma: Numeric = 0.7,
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size: Tuple[Numeric, Numeric] = (256, 256),
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return_array: bool = False) -> Union[np.ndarray, Image.Image]:
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"""Show a patch of imagery, with default options sensible for Landsat 8 imagery.
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For arguments and return value, see show_patch()
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"""
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ImageryVisualizer.show_patch(tile_reader, bands=bands, window=window,
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band_min=band_min, band_max=band_max, gamma=gamma,
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size=size, return_array=return_array)
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return ImageryVisualizer.show_patch(tile_reader, bands=bands, window=window,
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band_min=band_min, band_max=band_max, gamma=gamma,
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size=size, return_array=return_array)
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@staticmethod
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def show_sentinel2_patch(tile_reader: rasterio.DatasetReader,
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@ -253,9 +256,9 @@ class ImageryVisualizer(object):
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For arguments and return value, see show_patch()
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"""
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ImageryVisualizer.show_patch(tile_reader, bands=bands, window=window,
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band_min=band_min, band_max=band_max, gamma=gamma,
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size=size, return_array=return_array)
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return ImageryVisualizer.show_patch(tile_reader, bands=bands, window=window,
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band_min=band_min, band_max=band_max, gamma=gamma,
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size=size, return_array=return_array)
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@staticmethod
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def stat_landsat_tile(tile_tif, n_bins=40):
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@ -175,6 +175,19 @@ def get_resource_type_from_uri(sas_uri: str) -> Optional[str]:
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return None
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def get_endpoint_suffix(sas_uri):
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"""Gets the endpoint at which the blob storage account is served.
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Args:
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sas_uri: str, Azure blob storage URI with SAS token
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Returns: A string, usually 'core.windows.net' or 'core.chinacloudapi.cn', to use for the
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`endpoint` argument in various blob storage SDK functions.
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"""
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url_parts = parse.urlsplit(sas_uri)
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suffix = url_parts.netloc.split('.blob.')[1].split('/')[0]
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return suffix
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def get_permissions_from_uri(sas_uri: str) -> Set[str]:
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"""Get the permissions given by this SAS token.
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