updated tiling to work with latest kornia version

Former-commit-id: 2360cc0

Author: franioli

.flake8

@@ -1,5 +1,6 @@
 [flake8]
-ignore = E501
+select = E9,F63,F7,F82
+ignore = E501, E121, E123
 exclude = .git,__pycache__,.ipynb_checkpoints
 per-file-ignores =
     src/deep_image_matching/thirdparty/RoMa/roma/losses/robust_loss.py:E9,F63,F7,F82

.pre-commit-config.yaml

@@ -1,10 +1,14 @@
 repos:
+  - repo: https://github.com/psf/black
+    rev: 23.12.1
+    hooks:
+      - id: black
+  # - repo: https://github.com/pycqa/flake8
+  #   rev: 7.0.0 # Pick a git hash / tag to point to
+  #   hooks:
+  #     - id: flake8
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1
     hooks:
       - id: nbstripout
         files: ".ipynb"
-  - repo: https://github.com/psf/black
-    rev: 23.3.0
-    hooks:
-      - id: black

CONTRIBUTING.md

@@ -10,7 +10,7 @@
 
 2. Initialize the pre-commit hooks
 
-    Please initialize the pre-commit hooks, which will automatically run the linters (check for code errors) and formatting (with blakc) before each commit.
+    Please initialize the pre-commit hooks, which will automatically run the flake8 linter (check for code errors) and formatting (with blakc) before each commit.
 
     ```bash
     pre-commit install
@@ -24,18 +24,21 @@
     git checkout -b <branch-name>
     ```
 
-4. Make your changes
+4. Make your changes, format your code, and run the tests
 
-    Make your changes.
+    Make your changes (VSCode is strongly recommended as an editor)
     Please, format your code with a formatter (e.g. black).
     If you are using VSCode as your editor, you can install the Python extension, and set the formatter to black ([https://code.visualstudio.com/docs/python/formatting](https://code.visualstudio.com/docs/python/formatting)).
-    The pre-commit hooks will automatically run the linters and formatters before each commit, but better to already have the code well formatted before committing.
+    The pre-commit hooks will automatically run the linter and formatter before each commit.
+    If some code needs to be formatted, the pre-commit hooks stop the commit and format the code. You can then commit again (so better to already have the code well formatted before committing to avoid re-doing the commit).
     Then, make sure that the tests are passing. You can manually run the tests with pytest:
 
     ```bash
     python -m pytest
     ```
 
+    If you are using VSCode as your editor, you can also install the [Python extension](https://code.visualstudio.com/docs/python/testing), and run the tests from the editor.
+
 5. Push your changes to the remote repository
 
     Please push your changes to the remote repository, and open a pull request.

pyproject.toml

@@ -37,6 +37,7 @@ dependencies = [
     "exifread",
     "joblib",
     "pyyaml",
+    "packaging",    
 ]
 requires-python = ">=3.8"
 

src/deep_image_matching/utils/tiling.py

@@ -110,10 +110,10 @@ def compute_tiles_by_size(
 
         H, W = input.shape[2:]
 
-        # Compute padding to make the image divisible by the window size
-        # This returns a tuple of 4 int (top, bottom, left, right)
+        # Compute padding to make the image divisible by the window size.
+        # This returns a tuple of 2 int (vertical, horizontal)
+        # Note: from version 0.7.1 compute_padding() returns a tuple of 2 int and not 4 ints (top, bottom, left, right) anymore.
         padding = K.contrib.compute_padding((H, W), window_size)
-
         stride = [w - o for w, o in zip(window_size, overlap)]
         patches = K.contrib.extract_tensor_patches(
             input, window_size, stride=stride, padding=padding
@@ -122,15 +122,25 @@ def compute_tiles_by_size(
         # Remove batch dimension
         patches = patches.squeeze(0)
 
+        # Compute number of rows and columns (fallback if padding is not a tuple of 2 int)
+        if len(padding) == 2:
+            n_rows = (H + padding[0] + padding[1] - window_size[0]) // stride[0] + 1
+            n_cols = (W + padding[0] + padding[1] - window_size[1]) // stride[1] + 1
+        elif len(padding) == 4:
+            n_rows = (H + padding[0] + padding[1] - window_size[0]) // stride[0] + 1
+            n_cols = (W + padding[2] + padding[3] - window_size[1]) // stride[1] + 1
+
         # compute x,y coordinates of the top-left corner of each tile in the original image (before padding)
         origins = {}
-        n_rows = (H + padding[0] + padding[1] - window_size[0]) // stride[0] + 1
-        n_cols = (W + padding[2] + padding[3] - window_size[1]) // stride[1] + 1
         for row in range(n_rows):
             for col in range(n_cols):
                 tile_idx = np.ravel_multi_index((row, col), (n_rows, n_cols), order="C")
-                x = -padding[2] + col * stride[1]
-                y = -padding[0] + row * stride[0]
+                if len(padding) == 2:
+                    x = -padding[1] + col * stride[1]
+                    y = -padding[0] + row * stride[0]
+                elif len(padding) == 4:
+                    x = -padding[2] + col * stride[1]
+                    y = -padding[0] + row * stride[0]
                 origins[tile_idx] = (x, y)
 
         # Convert patches to numpy array (H, W, C)
@@ -206,14 +216,4 @@ def compute_tiles_auto(self, input: Union[np.ndarray, torch.Tensor]):
     )
     print(origins)
 
-    img_path = Path("data/belv_lingua_easy/DJI_20220728115852_0003.JPG")
-    img = cv2.imread(str(img_path))
-
-    tile_size = (2048, 2730)
-    overlap = 50
-    tiler = Tiler()
-    tiles, origins, padding = tiler.compute_tiles_by_size(
-        input=img, window_size=tile_size, overlap=overlap
-    )
-
     print("done")

tests/test_tiling.py

@@ -1,7 +1,7 @@
+import kornia
 import numpy as np
 import pytest
 import torch
-
 from deep_image_matching.utils.tiling import Tiler
 
 
@@ -10,6 +10,14 @@ def tiler():
     return Tiler()
 
 
+def recent_konria_version(base_version: str = "0.7.1"):
+    try:
+        from packaging import version
+    except ImportError:
+        return False
+    return version.parse(kornia.__version__) >= version.parse(base_version)
+
+
 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)
@@ -25,7 +33,10 @@ def test_compute_tiles_by_size_no_overlap_no_padding(tiler):
     assert isinstance(tiles, dict)
     assert isinstance(origins, dict)
     assert isinstance(padding, tuple)
-    assert len(padding) == 4
+    if recent_konria_version():
+        assert len(padding) == 2
+    else:
+        assert len(padding) == 4
 
     # Assert the number of tiles and origins
     assert len(tiles) == 4
@@ -36,7 +47,10 @@ def test_compute_tiles_by_size_no_overlap_no_padding(tiler):
         assert tile.shape == (window_size, window_size, 3)
 
     # Assert the padding values
-    assert padding == (0, 0, 0, 0)
+    if recent_konria_version():
+        assert padding == (0, 0)
+    else:
+        assert padding == (0, 0, 0, 0)
 
 
 def test_compute_tiles_by_size_no_overlap_padding(tiler):
@@ -54,8 +68,10 @@ def test_compute_tiles_by_size_no_overlap_padding(tiler):
     assert isinstance(tiles, dict)
     assert isinstance(origins, dict)
     assert isinstance(padding, tuple)
-    assert len(padding) == 4
-
+    if recent_konria_version():
+        assert len(padding) == 2
+    else:
+        assert len(padding) == 4
     # Assert the number of tiles and origins
     assert len(tiles) == 9
     assert len(origins) == 9
@@ -65,7 +81,10 @@ def test_compute_tiles_by_size_no_overlap_padding(tiler):
         assert tile.shape == (window_size, window_size, 3)
 
     # Assert the padding values
-    assert padding == (10, 10, 10, 10)
+    if recent_konria_version():
+        assert padding == (10, 10)
+    else:
+        assert padding == (10, 10, 10, 10)
 
 
 def test_compute_tiles_by_size_overlap_no_padding(tiler):
@@ -83,8 +102,10 @@ def test_compute_tiles_by_size_overlap_no_padding(tiler):
     assert isinstance(tiles, dict)
     assert isinstance(origins, dict)
     assert isinstance(padding, tuple)
-    assert len(padding) == 4
-
+    if recent_konria_version():
+        assert len(padding) == 2
+    else:
+        assert len(padding) == 4
     # Assert the number of tiles and origins
     assert len(tiles) == 4
     assert len(origins) == 4
@@ -94,7 +115,10 @@ def test_compute_tiles_by_size_overlap_no_padding(tiler):
         assert tile.shape == (window_size, window_size, 3)
 
     # Assert the padding values
-    assert padding == (0, 0, 0, 0)
+    if recent_konria_version():
+        assert padding == (0, 0)
+    else:
+        assert padding == (0, 0, 0, 0)
 
 
 def test_compute_tiles_by_size_with_torch_tensor(tiler):
@@ -113,8 +137,10 @@ def test_compute_tiles_by_size_with_torch_tensor(tiler):
     assert isinstance(tiles, dict)
     assert isinstance(origins, dict)
     assert isinstance(padding, tuple)
-    assert len(padding) == 4
-
+    if recent_konria_version():
+        assert len(padding) == 2
+    else:
+        assert len(padding) == 4
     # Assert the number of tiles and origins
     assert len(tiles) == 4
     assert len(origins) == 4
@@ -124,7 +150,10 @@ def test_compute_tiles_by_size_with_torch_tensor(tiler):
         assert tile.shape == (window_size[0], window_size[1], channels)
 
     # Assert the padding values
-    assert padding == (0, 0, 0, 0)
+    if recent_konria_version():
+        assert padding == (0, 0)
+    else:
+        assert padding == (0, 0, 0, 0)
 
 
 def test_compute_tiles_by_size_with_invalid_input(tiler):