mitoEM

Author: Donglai Wei

connectomics/data/dataset/dataset_volume_cached.py

@@ -80,8 +80,15 @@ def crop_volume(volume: np.ndarray, size: Tuple[int, ...], start: Tuple[int, ...
         else:
             full_pad_width = pad_width
 
-        # Use reflect padding to match the volume's edge values
-        cropped = np.pad(cropped, full_pad_width, mode="reflect")
+        # Check if cropped array is empty or has zero-sized dimensions
+        # Reflect padding cannot work on empty arrays, so fall back to constant padding
+        is_empty = cropped.size == 0 or any(s == 0 for s in cropped.shape)
+        if is_empty:
+            # Use constant padding for empty arrays (reflect padding requires at least one element)
+            cropped = np.pad(cropped, full_pad_width, mode="constant", constant_values=0)
+        else:
+            # Use reflect padding to match the volume's edge values
+            cropped = np.pad(cropped, full_pad_width, mode="reflect")
 
     # Final safety check: ensure output has exactly the requested size
     # This handles any edge cases where padding calculation might be off
@@ -100,7 +107,16 @@ def crop_volume(volume: np.ndarray, size: Tuple[int, ...], start: Tuple[int, ...
                 full_pad_width = [(0, 0)] + pad_needed
             else:
                 full_pad_width = pad_needed
-            cropped = np.pad(cropped, full_pad_width, mode="reflect")
+
+            # Check if cropped array is empty or has zero-sized dimensions
+            # Reflect padding cannot work on empty arrays, so fall back to constant padding
+            is_empty = cropped.size == 0 or any(s == 0 for s in cropped.shape)
+            if is_empty:
+                # Use constant padding for empty arrays (reflect padding requires at least one element)
+                cropped = np.pad(cropped, full_pad_width, mode="constant", constant_values=0)
+            else:
+                # Use reflect padding to match the volume's edge values
+                cropped = np.pad(cropped, full_pad_width, mode="reflect")
 
         # If still wrong (shouldn't happen), trim excess
         if has_channel:

scripts/convert_tiff_to_h5.py

@@ -0,0 +1,106 @@
+#!/usr/bin/env python3
+"""
+Convert a directory of image files (TIFF, PNG, JPG, etc.) into a single 3D HDF5 volume.
+
+Supports various image formats:
+- TIFF (single or multi-page)
+- PNG
+- JPEG/JPG
+- BMP
+- And any other formats supported by the I/O backend
+
+Usage:
+    python scripts/images_to_h5.py <input_pattern> <output_file.h5> [dataset_key]
+
+Examples:
+    # TIFF files (default dataset key: 'main')
+    python scripts/images_to_h5.py "datasets/bouton-lv/LV_EM/*.tiff" datasets/bouton-lv/LV_EM.h5
+
+    # PNG files (default dataset key: 'main')
+    python scripts/images_to_h5.py "/path/to/images/*.png" output.h5
+
+    # Custom dataset key
+    python scripts/images_to_h5.py "/path/to/labels/*.png" labels.h5 labels
+
+Note: Use quotes around the input pattern to prevent shell expansion.
+"""
+
+import sys
+import os
+from pathlib import Path
+from connectomics.data.io import read_volume, write_hdf5
+
+
+def main():
+    """Main conversion function."""
+    if len(sys.argv) < 3:
+        print("Usage: python scripts/images_to_h5.py <input_pattern> <output_file.h5> [dataset_key]")
+        print("")
+        print("Examples:")
+        print('  python scripts/images_to_h5.py "datasets/images/*.tiff" output.h5')
+        print('  python scripts/images_to_h5.py "/path/to/images/*.png" output.h5')
+        print('  python scripts/images_to_h5.py "/path/to/labels/*.png" labels.h5 labels')
+        print("")
+        print("Note: Use quotes around the input pattern to prevent shell expansion.")
+        sys.exit(1)
+
+    input_pattern = sys.argv[1]
+    output_file = sys.argv[2]
+    dataset_key = sys.argv[3] if len(sys.argv) > 3 else "main"
+
+    # Ensure output directory exists
+    output_dir = os.path.dirname(output_file)
+    if output_dir and not os.path.exists(output_dir):
+        print(f"Creating output directory: {output_dir}")
+        os.makedirs(output_dir, exist_ok=True)
+
+    # Detect file format from pattern
+    pattern_lower = input_pattern.lower()
+    if any(ext in pattern_lower for ext in ['.tif', '.tiff']):
+        format_name = "TIFF"
+    elif '.png' in pattern_lower:
+        format_name = "PNG"
+    elif any(ext in pattern_lower for ext in ['.jpg', '.jpeg']):
+        format_name = "JPEG"
+    else:
+        format_name = "image"
+
+    print(f"Reading {format_name} files matching: {input_pattern}")
+    print("This may take a while for large volumes...")
+
+    # Read all image files as a 3D volume
+    try:
+        volume = read_volume(input_pattern)
+    except Exception as e:
+        print(f"Error reading images: {e}")
+        print("\nTips:")
+        print("  - Check that the file pattern is correct")
+        print("  - Ensure all images have the same dimensions")
+        print("  - Verify the image files are readable")
+        sys.exit(1)
+
+    print(f"\n{'='*60}")
+    print(f"Volume Information:")
+    print(f"{'='*60}")
+    print(f"  Shape:      {volume.shape}")
+    print(f"  Data type:  {volume.dtype}")
+    print(f"  Size:       {volume.nbytes / (1024**3):.2f} GB")
+    print(f"  Min value:  {volume.min()}")
+    print(f"  Max value:  {volume.max()}")
+    print(f"{'='*60}")
+
+    print(f"\nSaving to: {output_file}")
+    print(f"Dataset key: '{dataset_key}'")
+
+    # Save as HDF5
+    try:
+        write_hdf5(output_file, volume, dataset=dataset_key)
+        print(f"\n✓ Successfully saved 3D volume to {output_file}")
+        print(f"  You can access it with h5py using: f['{dataset_key}']")
+    except Exception as e:
+        print(f"\n✗ Error writing HDF5 file: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()