File size: 9,195 Bytes
2b9ff22 | 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 | """
Dataset and data loading for S2F training.
Expects folder structure: each subfolder has BF_001.tif (bright field), *_gray.jpg (heatmap), and optionally .txt (cell_area, sum_force).
"""
import os
import cv2
import torch
from torch.utils.data import Dataset, DataLoader
from sklearn.model_selection import train_test_split
from concurrent.futures import ThreadPoolExecutor
import numpy as np
from utils import config
def blur_force_map(force_map, ksize=25, sigma=10):
if ksize % 2 == 0:
ksize += 1
if force_map.dim() == 3:
force_map = force_map.unsqueeze(0)
device = force_map.device
force_map = force_map.cpu()
blurred_maps = []
for i in range(force_map.size(0)):
force_np = force_map[i, 0].numpy().astype(np.float32)
blurred = cv2.GaussianBlur(force_np, (ksize, ksize), sigmaX=sigma)
blurred_maps.append(blurred)
return torch.from_numpy(np.stack(blurred_maps)).to(device)
class ImageDataset(Dataset):
def __init__(self, image_pairs, transform=None, channel_first=True,
blur_heatmap=False, threshold=0.0, return_metadata=False):
self.image_pairs = image_pairs
self.transform = transform
self.channel_first = channel_first
self.blur_heatmap = blur_heatmap
self.threshold = threshold
self.return_metadata = return_metadata
def __len__(self):
return len(self.image_pairs)
def __getitem__(self, idx):
if self.return_metadata:
bf_image, hm_image, numbers, metadata = self.image_pairs[idx]
else:
bf_image, hm_image, numbers = self.image_pairs[idx]
if isinstance(numbers, tuple):
cell_area, sum_force = numbers
else:
cell_area = 0
sum_force = numbers
image = torch.from_numpy(bf_image).float().unsqueeze(0)
heatmap = torch.from_numpy(hm_image).float().unsqueeze(0)
if self.transform:
image, heatmap = self.transform(image, heatmap)
cell_area = torch.tensor(cell_area, dtype=torch.float32)
sum_force = torch.tensor(sum_force, dtype=torch.float32)
heatmap[heatmap <= self.threshold] = 0
if self.blur_heatmap:
heatmap = blur_force_map(heatmap)
if not self.channel_first:
image = image.permute(2, 1, 0)
heatmap = heatmap.permute(2, 1, 0)
if self.return_metadata:
return image, heatmap, cell_area, sum_force, metadata
return image, heatmap, cell_area, sum_force
def load_image(filepath, target_size):
img = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
if isinstance(target_size, int):
target_size = (target_size, target_size)
img = cv2.resize(img, target_size)
img = img / 255.0
return img.astype(np.float32)
def load_text_data(filepath):
with open(filepath, 'r') as f:
lines = [line.strip() for line in f if line.strip()]
cell_area_diff = float(lines[0].split(":")[1].strip()) * config.SCALE_FACTOR_AREA
sum_force_diff = float(lines[1].split(":")[1].strip()) * config.SCALE_FACTOR_FORCE
return (cell_area_diff, sum_force_diff)
def load_images_from_subfolders(root_folder, target_size, load_numerical_data=True,
load_force_sum=False, return_metadata=False, substrate=None):
paired_images = []
numerical_data = []
metadata = []
for subfolder in os.listdir(root_folder):
subfolder_path = os.path.join(root_folder, subfolder)
if not os.path.isdir(subfolder_path):
continue
bf_image_path = hm_image_path = txt_file_path = None
for filename in os.listdir(subfolder_path):
if filename.endswith("BF_001.tif"):
bf_image_path = os.path.join(subfolder_path, filename)
elif filename.endswith("_gray.jpg"):
hm_image_path = os.path.join(subfolder_path, filename)
elif filename.endswith(".txt"):
txt_file_path = os.path.join(subfolder_path, filename)
if return_metadata:
if substrate is None:
from utils.substrate_settings import list_substrates
raise ValueError("substrate must be passed when return_metadata=True. Options: " +
", ".join(list_substrates()))
metadata.append({'folder_name': subfolder, 'substrate': substrate, 'root_folder': root_folder})
if load_numerical_data:
if bf_image_path and hm_image_path and txt_file_path:
paired_images.append((bf_image_path, hm_image_path))
numerical_data.append(load_text_data(txt_file_path))
elif load_force_sum:
if bf_image_path and hm_image_path:
paired_images.append((bf_image_path, hm_image_path))
hm = load_image(hm_image_path, target_size)
numerical_data.append((0, float(np.sum(hm)) * config.SCALE_FACTOR_FORCE))
else:
if bf_image_path and hm_image_path:
paired_images.append((bf_image_path, hm_image_path))
with ThreadPoolExecutor() as executor:
bf_loaded = list(executor.map(lambda p: load_image(p[0], target_size), paired_images))
hm_loaded = list(executor.map(lambda p: load_image(p[1], target_size), paired_images))
if not numerical_data:
numerical_data = [(0, 0)] * len(bf_loaded)
if return_metadata:
return list(zip(bf_loaded, hm_loaded, numerical_data, metadata))
return list(zip(bf_loaded, hm_loaded, numerical_data))
def prepare_data(input_folder, batch_size=8, target_size=(1024, 1024), split_size=0.2,
use_augmentations=True, train_test_sep_folder=True, channel_first=True,
load_numerical_data=False, load_force_sum=False, blur_heatmap=False,
threshold=0.0, return_metadata=False, substrate=None):
if load_numerical_data and load_force_sum:
raise ValueError("load_numerical_data and load_force_sum cannot be True at the same time")
if train_test_sep_folder:
train_folder = os.path.join(input_folder, 'train')
test_folder = os.path.join(input_folder, 'test')
if not (os.path.exists(train_folder) and os.path.exists(test_folder)):
raise ValueError(f"train/test folders not found in {input_folder}")
train_pairs = load_images_from_subfolders(train_folder, target_size=target_size,
load_numerical_data=load_numerical_data,
load_force_sum=load_force_sum,
return_metadata=return_metadata, substrate=substrate)
val_pairs = load_images_from_subfolders(test_folder, target_size=target_size,
load_numerical_data=load_numerical_data,
load_force_sum=load_force_sum,
return_metadata=return_metadata, substrate=substrate)
else:
image_pairs = load_images_from_subfolders(input_folder, target_size=target_size,
load_numerical_data=load_numerical_data,
load_force_sum=load_force_sum,
return_metadata=return_metadata, substrate=substrate)
train_pairs, val_pairs = train_test_split(image_pairs, test_size=split_size, random_state=42)
train_transform = None
if use_augmentations:
from .augmentations import AdvancedAugmentations
train_transform = AdvancedAugmentations(target_size)
train_dataset = ImageDataset(train_pairs, transform=train_transform, channel_first=channel_first,
blur_heatmap=blur_heatmap, threshold=threshold, return_metadata=return_metadata)
train_dataset.name = os.path.basename(input_folder)
val_dataset = ImageDataset(val_pairs, channel_first=channel_first,
blur_heatmap=blur_heatmap, threshold=threshold, return_metadata=return_metadata)
val_dataset.name = os.path.basename(input_folder)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader
def load_folder_data(folder_path, substrate=None, img_size=1024, blur_heatmap=False,
batch_size=2, threshold=0.0, return_metadata=False):
val_pairs = load_images_from_subfolders(folder_path, target_size=img_size,
load_numerical_data=False, load_force_sum=False,
return_metadata=return_metadata, substrate=substrate)
val_dataset = ImageDataset(val_pairs, channel_first=True, blur_heatmap=blur_heatmap,
threshold=threshold, return_metadata=return_metadata)
val_dataset.name = os.path.basename(folder_path)
return DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
|