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