sampling_segmentation.py: remove unused mask_image() function (#314)

NRCan · Jun 7, 2022 · 406771c · 406771c
1 parent 682bdc7
commit 406771c
Showing 1 changed file with 0 additions and 34 deletions.
diff --git a/sampling_segmentation.py b/sampling_segmentation.py
@@ -27,35 +27,6 @@
 np.random.seed(1234)
 
 
-def mask_image(arrayA, arrayB):
-    """Function to mask values of arrayB, based on 0 values from arrayA.
-
-    >>> x1 = np.array([0, 2, 4, 6, 0, 3, 9, 8], dtype=np.uint8).reshape(2,2,2)
-    >>> x2 = np.array([1.5, 1.2, 1.6, 1.2, 11., 1.1, 25.9, 0.1], dtype=np.float32).reshape(2,2,2)
-    >>> mask_image(x1, x2)
-    array([[[ 0. ,  0. ],
-            [ 1.6,  1.2]],
-    <BLANKLINE>
-           [[ 0. ,  0. ],
-            [25.9,  0.1]]], dtype=float32)
-    """
-
-    # Handle arrayA of shapes (h,w,c) and (h,w)
-    if len(arrayA.shape) == 3:
-        mask = arrayA[:, :, 0] != 0
-    else:
-        mask = arrayA != 0
-
-    ma_array = np.zeros(arrayB.shape, dtype=arrayB.dtype)
-    # Handle arrayB of shapes (h,w,c) and (h,w)
-    if len(arrayB.shape) == 3:
-        for i in range(0, arrayB.shape[2]):
-            ma_array[:, :, i] = mask * arrayB[:, :, i]
-    else:
-        ma_array = arrayB * mask
-    return ma_array
-
-
 def validate_class_prop_dict(actual_classes_dict, config_dict):
     """
     Populate dictionary containing class values found in vector data with values (thresholds) from sample/class_prop
@@ -400,7 +371,6 @@ def main(cfg: DictConfig) -> None:
     # OTHER PARAMETERS
     # TODO class_prop get_key_def('class_proportion', params['sample']['sampling_method'], None, expected_type=dict)
     class_prop = None
-    mask_reference = False  # TODO get_key_def('mask_reference', params['sample'], default=False, expected_type=bool)
     # set dontcare (aka ignore_index) value
     dontcare = cfg.dataset.ignore_index if cfg.dataset.ignore_index is not None else -1
     if dontcare == 0:
@@ -520,10 +490,6 @@ def main(cfg: DictConfig) -> None:
                 with rasterio.open(out_tif, "w", **out_meta) as dest:
                     dest.write(np_label_debug)
 
-            # Mask the zeros from input image into label raster.
-            if mask_reference:
-                np_label_raster = mask_image(np_input_image, np_label_raster)
-
             if aoi.split == 'trn':
                 out_file = trn_hdf5
             elif aoi.split == 'tst':