Apply black formatting

moondream/torch/image_crops.py

@@ -109,25 +109,29 @@ def overlap_crop_image(
     )
 
     # Convert to vips for resizing
-    vips_image = pyvips.Image.new_from_memory(image.tobytes(), 
-                                            image.shape[1], image.shape[0], 
-                                            image.shape[2], 'uchar')
-    
+    vips_image = pyvips.Image.new_from_memory(
+        image.tobytes(), image.shape[1], image.shape[0], image.shape[2], "uchar"
+    )
+
     # Resize using vips
     scale_x = target_size[1] / image.shape[1]
     scale_y = target_size[0] / image.shape[0]
     resized = vips_image.resize(scale_x, vscale=scale_y)
-    image = np.ndarray(buffer=resized.write_to_memory(),
-                      dtype=np.uint8,
-                      shape=[resized.height, resized.width, resized.bands])
+    image = np.ndarray(
+        buffer=resized.write_to_memory(),
+        dtype=np.uint8,
+        shape=[resized.height, resized.width, resized.bands],
+    )
 
     # Create global crop
     scale_x = base_size[1] / vips_image.width
     scale_y = base_size[0] / vips_image.height
     global_vips = vips_image.resize(scale_x, vscale=scale_y)
-    global_crop = np.ndarray(buffer=global_vips.write_to_memory(),
-                          dtype=np.uint8,
-                          shape=[global_vips.height, global_vips.width, global_vips.bands])
+    global_crop = np.ndarray(
+        buffer=global_vips.write_to_memory(),
+        dtype=np.uint8,
+        shape=[global_vips.height, global_vips.width, global_vips.bands],
+    )
 
     # Extract crops with overlap
     crops = []

tests/test_image_crops.py

@@ -2,6 +2,7 @@
 import torch
 from moondream.torch.image_crops import overlap_crop_image, reconstruct_from_crops
 
+
 def test_overlap_crop_basic():
     # Create a test image
     test_image = np.zeros((800, 600, 3), dtype=np.uint8)
@@ -16,16 +17,18 @@ def test_overlap_crop_basic():
     assert all(crop.shape == (378, 378, 3) for crop in result["local_crops"])
     assert len(result["tiling"]) == 2
 
+
 def test_overlap_crop_small_image():
     # Test with image smaller than crop size
     test_image = np.zeros((300, 200, 3), dtype=np.uint8)
     result = overlap_crop_image(test_image, overlap_margin=4, max_crops=12)
-    
+
     # Should still produce valid output
     assert result["global_crop"].shape == (378, 378, 3)
     assert len(result["local_crops"]) == 1
     assert result["tiling"] == (1, 1)
 
+
 def test_reconstruction():
     # Create a test image
     test_image = np.zeros((800, 600, 3), dtype=np.uint8)
@@ -35,7 +38,9 @@ def test_reconstruction():
     # Crop and reconstruct
     result = overlap_crop_image(test_image, overlap_margin=4, max_crops=12)
     crops_tensor = [torch.from_numpy(crop) for crop in result["local_crops"]]
-    reconstructed = reconstruct_from_crops(crops_tensor, result["tiling"], overlap_margin=4)
+    reconstructed = reconstruct_from_crops(
+        crops_tensor, result["tiling"], overlap_margin=4
+    )
 
     # Convert back to numpy for comparison
     reconstructed_np = reconstructed.numpy()
@@ -45,9 +50,9 @@ def test_reconstruction():
     # but the white rectangle should still be visible in the middle
     center_original = test_image[300:500, 200:400].mean()
     center_reconstructed = reconstructed_np[
-        reconstructed_np.shape[0]//2-100:reconstructed_np.shape[0]//2+100,
-        reconstructed_np.shape[1]//2-100:reconstructed_np.shape[1]//2+100
+        reconstructed_np.shape[0] // 2 - 100 : reconstructed_np.shape[0] // 2 + 100,
+        reconstructed_np.shape[1] // 2 - 100 : reconstructed_np.shape[1] // 2 + 100,
     ].mean()
-    
+
     # The center region should be significantly brighter than the edges
-    assert center_reconstructed > reconstructed_np[:100, :100].mean() + 100
+    assert center_reconstructed > reconstructed_np[:100, :100].mean() + 100