From 5031039b5081f2603ec06ca2da5abbc0dfccf325 Mon Sep 17 00:00:00 2001
From: Francesco Ioli <francesco.ioli@polimi.it>
Date: Wed, 24 Jan 2024 10:31:55 +0100
Subject: [PATCH] add tests for tiling

not convinced with the tests with overlap...
---
 src/deep_image_matching/utils/tiling.py |   3 +
 tests/test_tiling.py                    | 148 ++++++++++++++++++++++++
 2 files changed, 151 insertions(+)
 create mode 100644 tests/test_tiling.py

diff --git a/src/deep_image_matching/utils/tiling.py b/src/deep_image_matching/utils/tiling.py
index c4f6ba3e..c8fea665 100644
--- a/src/deep_image_matching/utils/tiling.py
+++ b/src/deep_image_matching/utils/tiling.py
@@ -89,6 +89,9 @@ def compute_tiles_by_size(
 
         if isinstance(overlap, int):
             overlap = (overlap, overlap)
+        elif isinstance(overlap, tuple) or isinstance(window_size, List):
+            # transpose to be (H, W)
+            overlap = (overlap[1], overlap[0])
         elif not isinstance(overlap, tuple) or isinstance(window_size, List):
             raise TypeError("overlap must be an integer or a tuple of integers")
         overlap = overlap
diff --git a/tests/test_tiling.py b/tests/test_tiling.py
new file mode 100644
index 00000000..50f3f9dc
--- /dev/null
+++ b/tests/test_tiling.py
@@ -0,0 +1,148 @@
+import numpy as np
+import pytest
+import torch
+
+from deep_image_matching.utils.tiling import Tiler
+
+
+@pytest.fixture
+def tiler():
+    return Tiler()
+
+
+def test_compute_tiles_by_size_no_overlap_no_padding(tiler):
+    # Create a numpy array with shape (100, 100, 3)
+    input_shape = (100, 100, 3)
+    input_image = np.random.randint(0, 255, input_shape, dtype=np.uint8)
+    window_size = 50
+    overlap = 0
+
+    tiles, origins, padding = tiler.compute_tiles_by_size(
+        input_image, window_size, overlap
+    )
+
+    # Assert the output types and shapes
+    assert isinstance(tiles, dict)
+    assert isinstance(origins, dict)
+    assert isinstance(padding, tuple)
+    assert len(padding) == 4
+
+    # Assert the number of tiles and origins
+    assert len(tiles) == 4
+    assert len(origins) == 4
+
+    # Assert the shape of the tiles
+    for tile in tiles.values():
+        assert tile.shape == (window_size, window_size, 3)
+
+    # Assert the padding values
+    assert padding == (0, 0, 0, 0)
+
+
+def test_compute_tiles_by_size_no_overlap_padding(tiler):
+    # Create a numpy array with shape (100, 100, 3)
+    input_shape = (100, 100, 3)
+    input_image = np.random.randint(0, 255, input_shape, dtype=np.uint8)
+    window_size = 40
+    overlap = 0
+
+    tiles, origins, padding = tiler.compute_tiles_by_size(
+        input_image, window_size, overlap
+    )
+
+    # Assert the output types and shapes
+    assert isinstance(tiles, dict)
+    assert isinstance(origins, dict)
+    assert isinstance(padding, tuple)
+    assert len(padding) == 4
+
+    # Assert the number of tiles and origins
+    assert len(tiles) == 9
+    assert len(origins) == 9
+
+    # Assert the shape of the tiles
+    for tile in tiles.values():
+        assert tile.shape == (window_size, window_size, 3)
+
+    # Assert the padding values
+    assert padding == (10, 10, 10, 10)
+
+
+def test_compute_tiles_by_size_overlap_no_padding(tiler):
+    # Create a numpy array with shape (100, 100, 3)
+    input_shape = (100, 100, 3)
+    input_image = np.random.randint(0, 255, input_shape, dtype=np.uint8)
+    window_size = 50
+    overlap = 10
+
+    tiles, origins, padding = tiler.compute_tiles_by_size(
+        input_image, window_size, overlap
+    )
+
+    # Assert the output types and shapes
+    assert isinstance(tiles, dict)
+    assert isinstance(origins, dict)
+    assert isinstance(padding, tuple)
+    assert len(padding) == 4
+
+    # Assert the number of tiles and origins
+    assert len(tiles) == 4
+    assert len(origins) == 4
+
+    # Assert the shape of the tiles
+    for tile in tiles.values():
+        assert tile.shape == (window_size, window_size, 3)
+
+    # Assert the padding values
+    assert padding == (0, 0, 0, 0)
+
+
+def test_compute_tiles_by_size_with_torch_tensor(tiler):
+    # Create a torch tensor with shape (3, 100, 100)
+    channels = 3
+    input_shape = (channels, 100, 100)
+    input_image = torch.randint(0, 255, input_shape, dtype=torch.uint8)
+    window_size = (50, 50)
+    overlap = (0, 0)
+
+    tiles, origins, padding = tiler.compute_tiles_by_size(
+        input_image, window_size, overlap
+    )
+
+    # Assert the output types and shapes
+    assert isinstance(tiles, dict)
+    assert isinstance(origins, dict)
+    assert isinstance(padding, tuple)
+    assert len(padding) == 4
+
+    # Assert the number of tiles and origins
+    assert len(tiles) == 4
+    assert len(origins) == 4
+
+    # Assert the shape of the tiles
+    for tile in tiles.values():
+        assert tile.shape == (window_size[0], window_size[1], channels)
+
+    # Assert the padding values
+    assert padding == (0, 0, 0, 0)
+
+
+def test_compute_tiles_by_size_with_invalid_input(tiler):
+    # Create an invalid window_size (a string)
+    input_image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+    window_size = "32"
+    overlap = 8
+
+    with pytest.raises(TypeError):
+        tiler.compute_tiles_by_size(input_image, window_size, overlap)
+
+    # Create an invalid overlap (a float)
+    window_size = 32
+    overlap = 8.0
+
+    with pytest.raises(TypeError):
+        tiler.compute_tiles_by_size(input_image, window_size, overlap)
+
+
+if __name__ == "__main__":
+    pytest.main([__file__])