回答編集履歴
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流下先配列の特定の座標に着目したとき、その座標の値が「4」ならば、一つ下の座標に侵食量の値がプラスされると、いうことかと思います。
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逆に言えば、ある特定座標の上の座標の値が「4」ならば、その座標に上の座標の侵食量の値がプラスされるということになります。
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したがって配列を畳み込み演算のように3*3のブロックごとに処理していけば良いです。
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```python
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from skimage.util import view_as_windows
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# 移動マップ
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d = np.array([[7, 0, 1],
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[6, 9, 2],
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[5, 4, 3]])[::-1, ::-1]
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m = np.array([[4, 4], [2, 8]]) # 流下先配列
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s = np.array([[1, 1], [1, 1]]) # 侵食量配列
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# 移動後の土砂配列の計算
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m_block = view_as_windows(np.pad(m, 1, constant_values=8), (3, 3))
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# [[8 8 8 8]
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# [8 4 4 8] [[888] [[888] [[844] [[448]
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# [8 2 8 8] [ [844] [448] [828] [288] ]
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# [8 8 8 8]] -> [828]], [288]], [888]], [888]]
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s_block = view_as_windows(np.pad(s, 1, constant_values=0), (3, 3))
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c = np.sum(((m_block == d) * s_block), (2, 3))
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c
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# array([[0, 0],
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# [1, 2]])
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```
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```python
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np.random.seed(0)
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m = np.random.randint(0, 9, (5, 5)) # 流下先配列
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# [[5 0 3 3 7]
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# [3 5 2 4 7]
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# [6 8 8 1 6]
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# [7 7 8 1 5]
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# [8 4 3 0 3]]
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s = np.random.randint(1, 3, (5, 5)) # 侵食量配列
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# [[2 1 2 2 1]
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# [1 2 1 2 2]
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# [2 2 2 1 2]
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# [1 2 2 2 2]
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# [1 2 1 1 2]]
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# 移動後の土砂配列の計算
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m_block = view_as_windows(np.pad(m, 1, constant_values=8), (3, 3))
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s_block = view_as_windows(np.pad(s, 1, constant_values=0), (3, 3))
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c = np.sum(((m_block == d) * s_block), (2, 3))
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# [[0 0 0 2 0]
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# [0 0 0 3 3]
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# [4 1 0 4 2]
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# [0 0 0 1 0]
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# [0 0 0 2 0]]
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```
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----
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（以下は以前の解答）
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全体的な処理フローは専門外のためよくわかってないですが（すみません）、
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とりあえず「赤枠内でやりたいこと」というのはこういうことでしょうか……？
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# [1, 2]])
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```
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```python
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# 701
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m = view_as_windows(d, (2, 2))
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# 70 | 01
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# 69 | 92
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# ---+---
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v[::-1, ::-1]
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m == v[::-1, ::-1]
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np.sum(m == v[::-1, ::-1], (2, 3))
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# 0 | 0
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# --+--
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# 1 | 2
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```
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全体的な処理フローは専門外のためよくわかってないですが（すみません）、
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とりあえず「赤枠内でやりたいこと」はこういうことでしょうか……？
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とりあえず「赤枠内でやりたいこと」というのはこういうことでしょうか……？
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# [1, 2]])
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```
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```python
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# ---+---
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# 00 | 00
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# ---+---
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# 0 | 0
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# --+--
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# 1 | 2
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```
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