171 lines
4.2 KiB
Python
171 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""
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Diagnostic script to check for scipy/numpy issues.
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Run this BEFORE starting the web app.
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Usage:
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python check_scipy.py
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"""
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import sys
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print(f"Python version: {sys.version}")
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print()
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# Check numpy
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try:
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import numpy as np
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print(f"NumPy version: {np.__version__}")
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print(f"NumPy config:")
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np.show_config()
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except ImportError as e:
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print(f"NumPy not installed: {e}")
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except Exception as e:
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print(f"NumPy error: {e}")
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print()
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print("-" * 50)
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print()
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# Check scipy
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try:
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import scipy
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print(f"SciPy version: {scipy.__version__}")
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except ImportError as e:
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print(f"SciPy not installed: {e}")
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print()
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# Check PIL
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try:
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from PIL import Image
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print(f"Pillow version: {Image.__version__}")
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except ImportError as e:
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print(f"Pillow not installed: {e}")
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print()
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print("-" * 50)
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print()
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# Test scipy DCT directly
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print("Testing scipy DCT...")
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try:
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from scipy.fftpack import dct, idct
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import numpy as np
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# Create test array
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test = np.random.rand(8, 8).astype(np.float64)
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print(f"Input array shape: {test.shape}, dtype: {test.dtype}")
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# Test 1D DCT
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row = test[0, :]
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result = dct(row, norm='ortho')
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print(f"1D DCT result shape: {result.shape}, dtype: {result.dtype}")
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# Test 2D DCT (the potentially problematic operation)
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result2d = dct(dct(test.T, norm='ortho').T, norm='ortho')
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print(f"2D DCT result shape: {result2d.shape}, dtype: {result2d.dtype}")
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# Test inverse
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recovered = idct(idct(result2d.T, norm='ortho').T, norm='ortho')
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error = np.max(np.abs(test - recovered))
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print(f"Round-trip error: {error}")
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if error < 1e-10:
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print("✓ scipy DCT working correctly")
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else:
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print("⚠ scipy DCT has precision issues")
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except Exception as e:
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print(f"✗ scipy DCT failed: {e}")
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import traceback
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traceback.print_exc()
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print()
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print("-" * 50)
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print()
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# Test with larger array (more like real image processing)
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print("Testing with larger arrays (512x512)...")
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try:
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from scipy.fftpack import dct, idct
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import numpy as np
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import gc
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# Simulate processing many 8x8 blocks
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large_array = np.random.rand(512, 512).astype(np.float64)
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print(f"Large array shape: {large_array.shape}, size: {large_array.nbytes} bytes")
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count = 0
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for y in range(0, 512, 8):
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for x in range(0, 512, 8):
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block = large_array[y:y+8, x:x+8].copy()
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dct_block = dct(dct(block.T, norm='ortho').T, norm='ortho')
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recovered = idct(idct(dct_block.T, norm='ortho').T, norm='ortho')
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large_array[y:y+8, x:x+8] = recovered
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count += 1
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print(f"Processed {count} blocks successfully")
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del large_array
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gc.collect()
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print("✓ Large array processing completed")
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except Exception as e:
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print(f"✗ Large array processing failed: {e}")
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import traceback
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traceback.print_exc()
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print()
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print("-" * 50)
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print()
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# Test PIL with large image
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print("Testing PIL with large image...")
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try:
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from PIL import Image
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import io
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# Create a large test image
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img = Image.new('RGB', (4000, 3000), color=(128, 128, 128))
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# Save to bytes
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buffer = io.BytesIO()
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img.save(buffer, format='PNG')
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img_bytes = buffer.getvalue()
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print(f"Test image size: {len(img_bytes)} bytes")
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# Re-open and process
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buffer2 = io.BytesIO(img_bytes)
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img2 = Image.open(buffer2)
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print(f"Re-opened image: {img2.size}, mode: {img2.mode}")
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# Convert to numpy array
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import numpy as np
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arr = np.array(img2)
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print(f"NumPy array: {arr.shape}, dtype: {arr.dtype}")
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# Clean up
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img.close()
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img2.close()
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buffer.close()
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buffer2.close()
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del arr
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gc.collect()
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print("✓ PIL large image test completed")
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except Exception as e:
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print(f"✗ PIL test failed: {e}")
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import traceback
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traceback.print_exc()
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print()
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print("=" * 50)
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print("Diagnostics complete")
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print()
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print("If no errors above but web app still crashes, try:")
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print("1. pip install --upgrade scipy numpy pillow")
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print("2. pip install scipy==1.11.4 numpy==1.26.4 # Known stable versions")
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print("3. Check if using conda vs pip (mixing can cause issues)")
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