Adding test scripts

2025-12-13 00:32:32 +02:00
parent b3b1e3acff
commit 2411223a14
5 changed files with 865 additions and 0 deletions
--- a/tests/test_16bit_tiff_loading.py
+++ b/tests/test_16bit_tiff_loading.py
@@ -0,0 +1,109 @@
+#!/usr/bin/env python3
+"""
+Test script for 16-bit TIFF loading and normalization.
+"""
+
+import numpy as np
+import tifffile
+from pathlib import Path
+import tempfile
+import sys
+import os
+
+# Add parent directory to path to import modules
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from src.utils.image import Image
+
+
+def create_test_16bit_tiff(output_path: str) -> str:
+    """Create a test 16-bit grayscale TIFF file.
+
+    Args:
+        output_path: Path where to save the test TIFF
+
+    Returns:
+        Path to the created TIFF file
+    """
+    # Create a 16-bit grayscale test image (100x100)
+    # With values ranging from 0 to 65535 (full 16-bit range)
+    height, width = 100, 100
+
+    # Create a gradient pattern
+    test_data = np.zeros((height, width), dtype=np.uint16)
+    for i in range(height):
+        for j in range(width):
+            # Create a diagonal gradient
+            test_data[i, j] = int((i + j) / (height + width - 2) * 65535)
+
+    # Save as TIFF
+    tifffile.imwrite(output_path, test_data)
+    print(f"Created test 16-bit TIFF: {output_path}")
+    print(f"  Shape: {test_data.shape}")
+    print(f"  Dtype: {test_data.dtype}")
+    print(f"  Min value: {test_data.min()}")
+    print(f"  Max value: {test_data.max()}")
+
+    return output_path
+
+
+def test_image_loading():
+    """Test loading 16-bit TIFF with the Image class."""
+    print("\n=== Testing Image Loading ===")
+
+    # Create temporary test file
+    with tempfile.NamedTemporaryFile(suffix=".tif", delete=False) as tmp:
+        test_path = tmp.name
+
+    try:
+        # Create test image
+        create_test_16bit_tiff(test_path)
+
+        # Load with Image class
+        print("\nLoading with Image class...")
+        img = Image(test_path)
+
+        print(f"Successfully loaded image:")
+        print(f"  Width: {img.width}")
+        print(f"  Height: {img.height}")
+        print(f"  Channels: {img.channels}")
+        print(f"  Dtype: {img.dtype}")
+        print(f"  Format: {img.format}")
+
+        # Test normalization
+        print("\nTesting normalization to float32 [0-1]...")
+        normalized = img.to_normalized_float32()
+
+        print(f"Normalized image:")
+        print(f"  Shape: {normalized.shape}")
+        print(f"  Dtype: {normalized.dtype}")
+        print(f"  Min value: {normalized.min():.6f}")
+        print(f"  Max value: {normalized.max():.6f}")
+        print(f"  Mean value: {normalized.mean():.6f}")
+
+        # Verify normalization
+        assert normalized.dtype == np.float32, "Dtype should be float32"
+        assert (
+            0.0 <= normalized.min() <= normalized.max() <= 1.0
+        ), "Values should be in [0, 1]"
+
+        print("\n✓ All tests passed!")
+        return True
+
+    except Exception as e:
+        print(f"\n✗ Test failed with error: {e}")
+        import traceback
+
+        traceback.print_exc()
+        return False
+
+    finally:
+        # Cleanup
+        if os.path.exists(test_path):
+            os.remove(test_path)
+            print(f"\nCleaned up test file: {test_path}")
+
+
+if __name__ == "__main__":
+    success = test_image_loading()
+    sys.exit(0 if success else 1)