Add initial support for bitwise masks on Landsat Collection 2 product…

…s (ref brazil-data-cube#172)

cube_builder/forms.py

@@ -72,6 +72,7 @@ class CustomMaskDefinition(Schema):
     nodata = fields.Integer(required=True, allow_none=False)
     saturated_data = fields.List(fields.Integer, required=False, allow_none=False)
     saturated_band = fields.String(required=False, allow_none=False)
+    bits = fields.Boolean(required=False, allow_none=False, default=False)
 
 
 class CubeParametersSchema(Schema):

cube_builder/maestro.py

@@ -483,10 +483,10 @@ def search_images(self, feature: str, start: str, end: str, tile_id: str):
                                 band_name_href = band.common_name
 
                             elif band.name not in feature['assets']:
-                                if f'sr_{band.name}' not in feature['assets']:
+                                if f'{band.name}.TIF' not in feature['assets']:
                                     continue
                                 else:
-                                    band_name_href = f'sr_{band.name}'
+                                    band_name_href = f'{band.name}.TIF'
 
                             scenes[band.name].setdefault(date, dict())
 

cube_builder/utils/image.py

@@ -288,12 +288,12 @@ def extract_qa_bits(band_data, bit_location, bit_length) -> numpy.ma.masked_arra
 
 def get_qa_mask(data: numpy.ma.masked_array, clear_data: List[float] = None, nodata: float = None) -> numpy.ma.masked_array:
     """Get the Raster Mask from any Landsat Quality Assessment product."""
-    is_numpy_array = type(data) in (numpy.ndarray, numpy.ma.masked_array)
+    is_numpy_or_masked_array = type(data) in (numpy.ndarray, numpy.ma.masked_array)
     if type(data) in (float, int,):
         data = numpy.ma.masked_array([data])
-    elif isinstance(data, Iterable) and not is_numpy_array:
-        data = numpy.ma.masked_array(data)
-    elif not is_numpy_array:
+    elif (isinstance(data, Iterable) and not is_numpy_or_masked_array) or (isinstance(data, numpy.ndarray) and not hasattr(data, 'mask')):
+        data = numpy.ma.masked_array(data, mask=data == nodata, fill_value=nodata)
+    elif not is_numpy_or_masked_array:
         raise TypeError(f'Expected a number or numpy masked array for {data}')
 
     result = data

cube_builder/utils/processing.py

@@ -726,7 +726,7 @@ def blend(activity, band_map, quality_band, build_clear_observation=False, block
 
             if mask_values['bits']:
                 m = numpy.ma.masked_array(mask, mask=mask == mask_values['nodata'], fill_value=mask_values['nodata'])
-                matched = get_qa_mask(m).mask
+                matched = get_qa_mask(m, clear_data=clear_values, nodata=mask_values['nodata'])
                 # Mark all invalid data
                 mask[matched.mask] = 0
                 # Mark all clear data as 1
@@ -1148,7 +1148,7 @@ def getMask(raster, dataset=None, mask=None, compute=False):
 
     efficacy, cloudratio = _qa_statistics(rastercm, mask=mask, compute=compute)
 
-    return rastercm.astype(numpy.uint8), efficacy, cloudratio
+    return rastercm, efficacy, cloudratio
 
 
 def parse_mask(mask: dict):