Adding test scripts
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tests/test_yolo_16bit_float32.py
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150
tests/test_yolo_16bit_float32.py
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#!/usr/bin/env python3
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"""
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Test script for YOLO preprocessing of 16-bit TIFF images with float32 passthrough.
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Verifies that no uint8 conversion occurs and data is preserved.
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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.model.yolo_wrapper import YOLOWrapper
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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 (200x200)
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# With specific values to test precision preservation
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height, width = 200, 200
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# Create a gradient pattern with the full 16-bit range
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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 using full 16-bit range
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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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print(
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f" Sample values: {test_data[50, 50]}, {test_data[100, 100]}, {test_data[150, 150]}"
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)
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return output_path
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def test_float32_passthrough():
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"""Test that 16-bit TIFF preprocessing passes float32 directly without uint8 conversion."""
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print("\n=== Testing Float32 Passthrough (NO uint8) ===")
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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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# Create YOLOWrapper instance
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print("\nTesting YOLOWrapper._prepare_source() for float32 passthrough...")
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wrapper = YOLOWrapper()
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# Call _prepare_source to preprocess the image
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prepared_source, cleanup_path = wrapper._prepare_source(test_path)
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print(f"\nPreprocessing result:")
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print(f" Original path: {test_path}")
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print(f" Prepared source type: {type(prepared_source)}")
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# Verify it returns a numpy array (not a file path)
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if isinstance(prepared_source, np.ndarray):
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print(
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f"\n✓ SUCCESS: Prepared source is a numpy array (float32 passthrough)"
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)
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print(f" Shape: {prepared_source.shape}")
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print(f" Dtype: {prepared_source.dtype}")
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print(f" Min value: {prepared_source.min():.6f}")
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print(f" Max value: {prepared_source.max():.6f}")
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print(f" Mean value: {prepared_source.mean():.6f}")
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# Verify it's float32 in [0, 1] range
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assert (
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prepared_source.dtype == np.float32
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), f"Expected float32, got {prepared_source.dtype}"
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assert (
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0.0 <= prepared_source.min() <= prepared_source.max() <= 1.0
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), f"Expected values in [0, 1], got [{prepared_source.min()}, {prepared_source.max()}]"
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# Verify it has 3 channels (RGB)
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assert (
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prepared_source.shape[2] == 3
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), f"Expected 3 channels (RGB), got {prepared_source.shape[2]}"
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# Verify no quantization to 256 levels (would happen with uint8 conversion)
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unique_values = len(np.unique(prepared_source))
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print(f" Unique values: {unique_values}")
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# With float32, we should have much more than 256 unique values
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if unique_values > 256:
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print(f"\n✓ SUCCESS: Data has {unique_values} unique values (> 256)")
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print(f" This confirms NO uint8 quantization occurred!")
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else:
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print(f"\n✗ WARNING: Data has only {unique_values} unique values")
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print(f" This might indicate uint8 quantization happened")
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# Sample some values to show precision
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print(f"\n Sample normalized values:")
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print(f" [50, 50]: {prepared_source[50, 50, 0]:.8f}")
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print(f" [100, 100]: {prepared_source[100, 100, 0]:.8f}")
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print(f" [150, 150]: {prepared_source[150, 150, 0]:.8f}")
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# No cleanup needed since we returned array directly
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assert (
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cleanup_path is None
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), "Cleanup path should be None for float32 pass through"
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print("\n✓ All float32 passthrough tests passed!")
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return True
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else:
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print(f"\n✗ FAILED: Prepared source is a file path: {prepared_source}")
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print(f" This means data was saved to disk, not passed as float32 array")
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if cleanup_path and os.path.exists(cleanup_path):
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os.remove(cleanup_path)
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return False
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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_float32_passthrough()
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sys.exit(0 if success else 1)
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