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f343f06 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | """
Per-modality preprocessing for satellite imagery.
Handles different channel counts:
- Optical RGB: 3 channels (R, G, B)
- SAR: 2 channels (VV, VH)
- Multispectral: 12 channels (Sentinel-2 bands)
"""
import torch
import torch.nn.functional as F
from torchvision import transforms
from PIL import Image
import numpy as np
# ImageNet normalization for RGB
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
# Sentinel-2 band statistics (approximate)
SENTINEL2_MEAN = [1353.0, 1117.0, 1042.0, 947.0, 1199.0, 1645.0, 1849.0, 1793.0, 1859.0, 1008.0, 1593.0, 1064.0]
SENTINEL2_STD = [235.0, 309.0, 392.0, 597.0, 490.0, 625.0, 736.0, 755.0, 846.0, 487.0, 561.0, 459.0]
# SAR statistics (approximate, in dB)
SAR_MEAN = [-12.0, -18.0]
SAR_STD = [5.0, 5.0]
def get_optical_transform(size: int = 224) -> transforms.Compose:
"""Get transforms for optical RGB images."""
return transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
])
def get_sar_transform(size: int = 224) -> transforms.Compose:
"""Get transforms for SAR images (VV/VH channels)."""
return transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize(mean=SAR_MEAN, std=SAR_STD)
])
def get_multispectral_transform(size: int = 224) -> transforms.Compose:
"""Get transforms for multispectral images (12 channels)."""
return transforms.Compose([
transforms.Resize(size),
transforms.CenterCrop(size),
transforms.ToTensor(),
transforms.Normalize(mean=SENTINEL2_MEAN, std=SENTINEL2_STD)
])
def preprocess_image(
image: Image.Image,
modality: str,
size: int = 224
) -> torch.Tensor:
"""
Preprocess image based on modality.
Args:
image: Input PIL image
modality: "optical", "sar", or "multispectral"
size: Output image size
Returns:
Preprocessed tensor
"""
# Handle channel mismatch before applying transform
if modality == "sar":
# SAR expects 2 channels, but PIL images are typically 3 channels
# Convert to numpy, take first 2 channels, convert back
img_array = np.array(image)
if img_array.shape[-1] == 3:
img_array = img_array[..., :2]
image = Image.fromarray(img_array)
transform = get_sar_transform(size)
elif modality == "optical":
transform = get_optical_transform(size)
elif modality == "multispectral":
transform = get_multispectral_transform(size)
else:
raise ValueError(f"Unknown modality: {modality}")
return transform(image)
def handle_channels(
image: np.ndarray,
target_channels: int,
modality: str
) -> np.ndarray:
"""
Handle channel mismatch for different modalities.
Args:
image: Input image array (H, W, C)
target_channels: Expected number of channels
modality: Modality type
Returns:
Image with correct number of channels
"""
current_channels = image.shape[-1] if len(image.shape) == 3 else 1
if current_channels == target_channels:
return image
# ponytail: simple channel handling, not perfect but works for v1
if modality == "optical" and current_channels >= 3:
# Take first 3 channels (RGB)
return image[..., :3]
elif modality == "sar" and current_channels >= 2:
# Take first 2 channels (VV, VH)
return image[..., :2]
elif modality == "multispectral":
if current_channels < target_channels:
# Pad with zeros
padding = np.zeros((*image.shape[:-1], target_channels - current_channels))
return np.concatenate([image, padding], axis=-1)
else:
# Take first 12 channels
return image[..., :target_channels]
return image
# Self-check
if __name__ == "__main__":
# Create dummy images for testing
dummy_rgb = Image.fromarray(np.random.randint(0, 255, (256, 256, 3), dtype=np.uint8))
dummy_sar = Image.fromarray(np.random.randint(0, 255, (256, 256, 2), dtype=np.uint8))
# Test preprocessing
optical_tensor = preprocess_image(dummy_rgb, "optical")
sar_tensor = preprocess_image(dummy_sar, "sar")
print(f"Optical shape: {optical_tensor.shape}") # Should be [3, 224, 224]
print(f"SAR shape: {sar_tensor.shape}") # Should be [2, 224, 224]
print("Preprocessing test passed!") |