Crossfade 2

aeronet_raster/aeronet_raster/dataadapters/filemixin.py

@@ -22,6 +22,16 @@ def __enter__(self):
     def __exit__(self, exc_type, exc_val, traceback):
         self.close()
 
+    def __getitem__(self, item):
+        if not self._descriptor:
+            raise ValueError(f'File {self._path} is not opened')
+        return super().__getitem__(item)
+
+    def __setitem__(self, item, data):
+        if not self._descriptor:
+            raise ValueError(f'File {self._path} is not opened')
+        super().__setitem__(item, data)
+
     @property
     def shape(self):
         if not self._descriptor:

aeronet_raster/aeronet_raster/dataadapters/imageadapter.py

@@ -11,7 +11,7 @@ def __init__(self, padding_mode='constant', **kwargs):
     def parse_item(self, item):
         item = super().parse_item(item)
         if not len(item) == 3:
-            raise ValueError(f"mage must be indexed with 3 axes, got {item}")
+            raise ValueError(f"Image must be indexed with 3 axes, got {item}")
         if isinstance(item[0], slice):
             item[0] = list(range(item[0].start, item[0].stop, item[0].step))
         assert isinstance(item[1], slice) and isinstance(item[2], slice),\
@@ -36,11 +36,11 @@ class ImageWriter(BoundSafeWriterMixin, AbstractWriter):
     def parse_item(self, item):
         item = super().parse_item(item)
         if not len(item) == 3:
-            raise ValueError(f"PIL Image must be indexed with 3 axes, got {item}")
+            raise ValueError(f"Image must be indexed with 3 axes, got {item}")
         if isinstance(item[0], slice):
             item[0] = list(range(item[0].start, item[0].stop, item[0].step))
         assert isinstance(item[1], slice) and isinstance(item[2], slice),\
-            f"PIL Image spatial axes (1 and 2) must be indexed with slices, got {item}"
+            f"Image spatial axes (1 and 2) must be indexed with slices, got {item}"
         return item
 
     @property

aeronet_raster/aeronet_raster/dataprocessor.py

@@ -70,9 +70,14 @@ def get_blend_mask(shape: Sequence[int], margin: Sequence[int]) -> np.ndarray:
             continue
         if margin[axis]*2 >= shape[axis]:
             raise ValueError(f'margin must be less than shape//2, got {margin[axis]}, {shape[axis]} along axis={axis}')
-        linear_mask = np.concatenate((np.linspace(1/margin[axis], 1, margin[axis]) if margin[axis] > 1 else np.array((0.5,)),
+        min_v = 1/(margin[axis] + 1)
+        linear_mask = np.concatenate((np.linspace(min_v,
+                                                  1 - min_v,
+                                                  margin[axis]) if margin[axis] > 1 else np.array((0.5,)),
                                       np.ones(shape[axis] - 2 * margin[axis]),
-                                      np.linspace(1, 1/margin[axis], margin[axis]) if margin[axis] > 1 else np.array((0.5,))))
+                                      np.linspace(1 - min_v,
+                                                  min_v,
+                                                  margin[axis]) if margin[axis] > 1 else np.array((0.5,))))
 
         mask = np.swapaxes(mask, len(shape)-1, axis)
         mask = mask*linear_mask
@@ -128,8 +133,9 @@ def process_image(src: ImageReader,
         dst_margin_mode: 'crop' or 'crossfade'
         verbose: verbose
     """
-    def build_sampler(shape, sample_size, margin):
-        stride = sample_size - 2 * margin
+    def build_sampler(shape, sample_size, margin, mode):
+        assert mode in DST_MARGIN_MODES
+        stride = sample_size - 2 * margin if mode == 'crop' else sample_size - margin
         assert np.all(stride > 0)
         safe_shape = get_safe_shape(shape, stride)
         return GridSampler(make_grid([(-margin[i],