File size: 7,814 Bytes
894fa47 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 | """
Image preprocessing utilities.
Handles image loading, resizing, normalization for model inference.
"""
import torch
import torchvision.transforms as transforms
from PIL import Image
import io
import logging
from typing import Union, Tuple
import numpy as np
logger = logging.getLogger(__name__)
# Tile extraction size (matches Kaggle SimpleSlideDataset standardize_transform)
TARGET_SIZE = 256
# ViT backbone input size (matches Kaggle test_transform: transforms.Resize(224))
# CRITICAL: The model was trained with 224x224 input to the ViT backbone,
# NOT 256x256. Using 256 here causes feature space mismatch and random predictions.
MODEL_INPUT_SIZE = 224
# ImageNet normalization statistics (used during model training)
IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]
# Preprocessing pipeline - matches Kaggle test_transform exactly:
# transforms.Resize(224), transforms.ToTensor(), transforms.Normalize(...)
preprocess_transform = transforms.Compose([
transforms.Resize((MODEL_INPUT_SIZE, MODEL_INPUT_SIZE)),
transforms.ToTensor(),
transforms.Normalize(mean=IMAGENET_MEAN, std=IMAGENET_STD)
])
# For standardization before model (matches training)
resize_transform = transforms.Resize((TARGET_SIZE, TARGET_SIZE))
def load_image_from_bytes(image_bytes: bytes) -> Image.Image:
"""
Load image from bytes.
Args:
image_bytes: Image data as bytes
Returns:
PIL Image in RGB format
Raises:
ValueError: If image cannot be loaded
"""
try:
image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
return image
except Exception as e:
logger.error(f"Failed to load image from bytes: {e}")
raise ValueError(f"Invalid image data: {e}")
def load_image_from_path(image_path: str) -> Image.Image:
"""
Load image from file path.
Args:
image_path: Path to image file
Returns:
PIL Image in RGB format
Raises:
FileNotFoundError: If file doesn't exist
ValueError: If image cannot be loaded
"""
try:
image = Image.open(image_path).convert('RGB')
return image
except FileNotFoundError:
logger.error(f"Image file not found: {image_path}")
raise
except Exception as e:
logger.error(f"Failed to load image from {image_path}: {e}")
raise ValueError(f"Invalid image file: {e}")
def preprocess_image(image: Image.Image) -> torch.Tensor:
"""
Preprocess single image for model inference.
Args:
image: PIL Image in RGB format
Returns:
Preprocessed tensor of shape (3, 224, 224)
Process:
1. Resize to 224x224 (matches Kaggle test_transform)
2. Convert to tensor
3. Normalize with ImageNet statistics
"""
try:
tensor = preprocess_transform(image)
return tensor
except Exception as e:
logger.error(f"Failed to preprocess image: {e}")
raise
def preprocess_images_batch(images: list) -> torch.Tensor:
"""
Preprocess batch of images (tiles).
Args:
images: List of PIL Images
Returns:
Batch tensor of shape (num_images, 3, 256, 256)
"""
tensors = []
for img in images:
try:
tensor = preprocess_image(img)
tensors.append(tensor)
except Exception as e:
logger.warning(f"Failed to preprocess image in batch: {e}")
continue
if not tensors:
raise ValueError("No valid images in batch")
return torch.stack(tensors)
def extract_tiles_from_image(
image: Image.Image,
tile_size: int = 256,
max_tiles: int = 1000
) -> list:
"""
Extract tile patches from a large image.
Useful for whole slide images (WSI) or large medical images.
Args:
image: PIL Image
tile_size: Size of each tile patch (256x256)
max_tiles: Maximum number of tiles to extract
Returns:
List of PIL Images (tiles)
"""
width, height = image.size
tiles = []
try:
# Extract non-overlapping tiles
for y in range(0, height, tile_size):
for x in range(0, width, tile_size):
if len(tiles) >= max_tiles:
break
# Extract tile with padding if at edges
right = min(x + tile_size, width)
bottom = min(y + tile_size, height)
tile = image.crop((x, y, right, bottom))
# Pad if necessary to maintain tile_size
if tile.size != (tile_size, tile_size):
padded_tile = Image.new('RGB', (tile_size, tile_size), color=(0, 0, 0))
padded_tile.paste(tile, (0, 0))
tile = padded_tile
tiles.append(tile)
if len(tiles) >= max_tiles:
break
logger.info(f"Extracted {len(tiles)} tiles from image ({width}x{height})")
return tiles
except Exception as e:
logger.error(f"Failed to extract tiles: {e}")
raise
def get_image_info(image: Image.Image) -> dict:
"""Get metadata about an image."""
return {
'size': image.size,
'width': image.width,
'height': image.height,
'mode': image.mode,
'format': image.format
}
class ImagePreprocessor:
"""Image preprocessing pipeline."""
def __init__(self,
target_size: int = MODEL_INPUT_SIZE,
mean: list = IMAGENET_MEAN,
std: list = IMAGENET_STD):
"""
Initialize preprocessor.
Args:
target_size: Target image size for ViT backbone input (224 to match Kaggle test_transform)
mean: Normalization mean values
std: Normalization std values
"""
self.target_size = target_size
self.mean = mean
self.std = std
self.transform = transforms.Compose([
transforms.Resize((target_size, target_size)),
transforms.ToTensor(),
transforms.Normalize(mean=mean, std=std)
])
def process(self, image_input: Union[str, bytes, Image.Image]) -> torch.Tensor:
"""
Process image from various input formats.
Args:
image_input: Image path (str), image bytes, or PIL Image
Returns:
Preprocessed tensor (3, 256, 256)
"""
# Load image if needed
if isinstance(image_input, str):
image = load_image_from_path(image_input)
elif isinstance(image_input, bytes):
image = load_image_from_bytes(image_input)
elif isinstance(image_input, Image.Image):
image = image_input
else:
raise TypeError(f"Unsupported input type: {type(image_input)}")
# Preprocess
return preprocess_image(image)
def process_batch(self, images: list) -> torch.Tensor:
"""
Process batch of images.
Args:
images: List of image inputs (paths, bytes, or PIL Images)
Returns:
Batch tensor (batch_size, 3, 256, 256)
"""
tensors = []
for img_input in images:
try:
tensor = self.process(img_input)
tensors.append(tensor)
except Exception as e:
logger.warning(f"Failed to process image: {e}")
continue
if not tensors:
raise ValueError("No images could be processed")
return torch.stack(tensors)
|