Add tests for image_crops and GitHub workflow

.github/workflows/test.yml

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+name: Tests
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.9", "3.10", "3.11", "3.12"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest
+        pip install -r requirements.txt
+    - name: Run tests
+      run: |
+        python -m pytest tests/test_image_crops.py -v

tests/test_image_crops.py

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+import numpy as np
+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)
+    # Add a recognizable pattern - white rectangle in the middle
+    test_image[300:500, 200:400] = 255
+
+    result = overlap_crop_image(test_image, overlap_margin=4, max_crops=12)
+
+    # Check basic properties
+    assert result["global_crop"].shape == (378, 378, 3)
+    assert len(result["local_crops"]) > 0
+    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)
+    # Add a recognizable pattern
+    test_image[300:500, 200:400] = 255
+
+    # 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)
+
+    # Convert back to numpy for comparison
+    reconstructed_np = reconstructed.numpy()
+
+    # The reconstructed image should be similar to the input
+    # We can't expect exact equality due to resizing operations
+    # 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
+    ].mean()
+
+    # The center region should be significantly brighter than the edges
+    assert center_reconstructed > reconstructed_np[:100, :100].mean() + 100