Add patient management, deployment scripts, and Docker fixes

brace-generator/image_loader.py

@@ -0,0 +1,115 @@
+"""
+Image loader supporting JPEG, PNG, and DICOM formats.
+"""
+import numpy as np
+from pathlib import Path
+from typing import Tuple, Optional
+
+try:
+    import pydicom
+    HAS_PYDICOM = True
+except ImportError:
+    HAS_PYDICOM = False
+
+try:
+    from PIL import Image
+    HAS_PIL = True
+except ImportError:
+    HAS_PIL = False
+
+try:
+    import cv2
+    HAS_CV2 = True
+except ImportError:
+    HAS_CV2 = False
+
+
+def load_xray(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+    """
+    Load an X-ray image from file.
+    
+    Supports: JPEG, PNG, BMP, DICOM (.dcm)
+    
+    Args:
+        path: Path to the image file
+        
+    Returns:
+        img_u8: Grayscale image as uint8 array (H, W)
+        spacing_mm: Pixel spacing [sx, sy] in mm, or None if not available
+    """
+    path = Path(path)
+    suffix = path.suffix.lower()
+    
+    # DICOM
+    if suffix in ['.dcm', '.dicom']:
+        if not HAS_PYDICOM:
+            raise ImportError("pydicom is required for DICOM files. Install with: pip install pydicom")
+        return _load_dicom(str(path))
+    
+    # Standard image formats
+    if suffix in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']:
+        return _load_standard_image(str(path))
+    
+    # Try to load as standard image anyway
+    try:
+        return _load_standard_image(str(path))
+    except Exception as e:
+        raise ValueError(f"Could not load image: {path}. Error: {e}")
+
+
+def _load_dicom(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+    """Load DICOM file."""
+    ds = pydicom.dcmread(path)
+    arr = ds.pixel_array.astype(np.float32)
+    
+    # Apply modality LUT if present
+    if hasattr(ds, 'RescaleSlope') and hasattr(ds, 'RescaleIntercept'):
+        arr = arr * ds.RescaleSlope + ds.RescaleIntercept
+    
+    # Normalize to 0-255
+    arr = arr - arr.min()
+    if arr.max() > 0:
+        arr = arr / arr.max()
+    img_u8 = (arr * 255).astype(np.uint8)
+    
+    # Get pixel spacing
+    spacing_mm = None
+    if hasattr(ds, 'PixelSpacing'):
+        # PixelSpacing is [row_spacing, col_spacing] in mm
+        sy, sx = [float(x) for x in ds.PixelSpacing]
+        spacing_mm = np.array([sx, sy], dtype=np.float32)
+    elif hasattr(ds, 'ImagerPixelSpacing'):
+        sy, sx = [float(x) for x in ds.ImagerPixelSpacing]
+        spacing_mm = np.array([sx, sy], dtype=np.float32)
+    
+    return img_u8, spacing_mm
+
+
+def _load_standard_image(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+    """Load standard image format (JPEG, PNG, etc.)."""
+    if HAS_CV2:
+        img = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
+        if img is None:
+            raise ValueError(f"Could not read image: {path}")
+        return img.astype(np.uint8), None
+    elif HAS_PIL:
+        img = Image.open(path).convert('L')  # Convert to grayscale
+        return np.array(img, dtype=np.uint8), None
+    else:
+        raise ImportError("Either opencv-python or Pillow is required. Install with: pip install opencv-python")
+
+
+def load_xray_rgb(path: str) -> Tuple[np.ndarray, Optional[np.ndarray]]:
+    """
+    Load X-ray as RGB (for models that expect 3-channel input).
+    
+    Returns:
+        img_rgb: RGB image as uint8 array (H, W, 3)
+        spacing_mm: Pixel spacing or None
+    """
+    img_gray, spacing = load_xray(path)
+    
+    # Convert grayscale to RGB by stacking
+    img_rgb = np.stack([img_gray, img_gray, img_gray], axis=-1)
+    
+    return img_rgb, spacing