Spaces:
Sleeping
Sleeping
File size: 9,156 Bytes
b2ffc9b |
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 |
from typing import List, Tuple
import os
import glob
import numpy as np
import pandas as pd
from PIL import Image
from scipy.ndimage.filters import gaussian_filter, median_filter, rank_filter
from torch.utils.data import Dataset
from torchvision import transforms
from utils.constants import Split, Columns, CropsColumns, ProbsColumns
from utils.paths import CROPS_DATASET, CROPS_PATH, COORDS_PATH, IMG_PATH, PROBS_DATASET, PROBS_PATH, HAADF_DATASET, PT_DATASET
class ImageClassificationDataset(Dataset):
def __init__(self, image_paths, image_labels, include_filename=False):
self.image_paths = image_paths
self.image_labels = image_labels
self.include_filename = include_filename
self.transform = transforms.Compose([
transforms.ToTensor()
# transforms.Normalize(mean=[0.5], std=[0.5])
])
def get_n_labels(self):
return len(set(self.image_labels))
def __len__(self):
return len(self.image_paths)
@staticmethod
def load_image(img_filename):
img = Image.open(img_filename)
np_img = np.asarray(img).astype(np.float32)
np_bg = median_filter(np_img, size=(40, 40))
np_clean = np_img - np_bg
np_normed = (np_clean - np_clean.min()) / (np_clean.max() - np_clean.min())
return np_normed
def __getitem__(self, idx):
img_path = self.image_paths[idx]
image = self.load_image(img_path)
image = self.transform(image)
label = self.image_labels[idx]
if self.include_filename:
return image, label, os.path.basename(img_path)
else:
return image, label
@staticmethod
def get_filenames_labels(split: Split) -> Tuple[List[str], List[int]]:
raise NotImplementedError
@classmethod
def train_dataset(cls, **kwargs):
filenames, labels = cls.get_filenames_labels(Split.TRAIN)
return cls(filenames, labels, **kwargs)
@classmethod
def val_dataset(cls, **kwargs):
filenames, labels = cls.get_filenames_labels(Split.VAL)
return cls(filenames, labels, **kwargs)
@classmethod
def test_dataset(cls, **kwargs):
filenames, labels = cls.get_filenames_labels(Split.TEST)
return cls(filenames, labels, **kwargs)
class HaadfDataset(ImageClassificationDataset):
@staticmethod
def get_filenames_labels(split: Split) -> Tuple[List[str], List[int]]:
df = pd.read_csv(HAADF_DATASET)
split_df = df[df[Columns.SPLIT] == split]
filenames = (IMG_PATH + os.sep + split_df[Columns.FILENAME]).to_list()
labels = (split_df[Columns.LABEL]).to_list()
return filenames, labels
class ImageDataset:
FILENAME_COL = "Filename"
SPLIT_COL = "Split"
RULER_UNITS = "Ruler Units"
def __init__(self, dataset_csv: str):
self.df = pd.read_csv(dataset_csv)
def iterate_data(self, split: Split):
df = self.df[self.df[self.SPLIT_COL] == split]
for idx, row in df.iterrows():
image_filename = os.path.join(IMG_PATH, row[self.FILENAME_COL])
yield image_filename
def get_ruler_units_by_img_name(self, name):
print(name)
return self.df[self.df[self.FILENAME_COL] == name][self.RULER_UNITS].values[0]
class CoordinatesDataset:
FILENAME_COL = "Filename"
COORDS_COL = "Coords"
SPLIT_COL = "Split"
def __init__(self, coord_image_csv: str):
self.df = pd.read_csv(coord_image_csv)
def iterate_data(self, split: Split):
df = self.df[self.df[self.SPLIT_COL] == split]
for idx, row in df.iterrows():
image_filename = os.path.join(IMG_PATH, row[self.FILENAME_COL])
if isinstance(row[self.COORDS_COL], str):
coords_filename = os.path.join(COORDS_PATH, row[self.COORDS_COL])
else:
coords_filename = None
yield image_filename, coords_filename
@staticmethod
def load_coordinates(label_filename: str) -> List[Tuple[int, int]]:
atom_coordinates = pd.read_csv(label_filename)
return list(zip(atom_coordinates['X'], atom_coordinates['Y']))
def split_length(self, split: Split):
df = self.df[self.df[self.SPLIT_COL] == split]
return len(df)
class HaadfCoordinates(CoordinatesDataset):
def __init__(self):
super().__init__(coord_image_csv=PT_DATASET)
class CropsDataset(ImageClassificationDataset):
@staticmethod
def get_filenames_labels(split: Split):
df = pd.read_csv(CROPS_DATASET)
split_df = df[df[CropsColumns.SPLIT] == split]
filenames = (CROPS_PATH + os.sep + split_df[CropsColumns.FILENAME]).to_list()
labels = (split_df[CropsColumns.LABEL]).to_list()
return filenames, labels
class CropsCustomDataset(ImageClassificationDataset):
@staticmethod
def get_filenames_labels(split: Split, crops_dataset: str, crops_path: str):
df = pd.read_csv(crops_dataset)
split_df = df[df[CropsColumns.SPLIT] == split]
filenames = (crops_path + os.sep + split_df[CropsColumns.FILENAME]).to_list()
labels = (split_df[CropsColumns.LABEL]).to_list()
return filenames, labels
class ProbsDataset(ImageClassificationDataset):
@staticmethod
def get_filenames_labels(split: Split):
df = pd.read_csv(PROBS_DATASET)
split_df = df[df[ProbsColumns.SPLIT] == split]
filenames = (PROBS_PATH + os.sep + split_df[ProbsColumns.FILENAME]).to_list()
labels = (split_df[ProbsColumns.LABEL]).to_list()
return filenames, labels
class SlidingCropDataset(Dataset):
def __init__(self, tif_filename, include_coords=True):
self.filename = tif_filename
self.include_coords = include_coords
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5])
])
self.n_labels = 2
self.step_size = 2
self.window_size = (21, 21)
self.loaded_crops = []
self.loaded_coords = []
self.load_crops()
def sliding_window(self, image):
# slide a window across the image
for x in range(0, image.shape[0] - self.window_size[0], self.step_size):
for y in range(0, image.shape[1] - self.window_size[1], self.step_size):
# yield the current window
center_x = x + ((self.window_size[0] - 1) // 2)
center_y = y + ((self.window_size[1] - 1) // 2)
yield center_x, center_y, image[x:x + self.window_size[0], y:y + self.window_size[1]]
@staticmethod
def load_image(img_filename):
img = Image.open(img_filename)
np_img = np.asarray(img).astype(np.float32)
np_bg = median_filter(np_img, size=(40, 40))
np_clean = np_img - np_bg
np_normed = (np_clean - np_clean.min()) / (np_clean.max() - np_clean.min())
return np_normed
def load_crops(self):
img = self.load_image(self.filename)
for x_center, y_center, img_crop in self.sliding_window(img):
self.loaded_crops.append(img_crop)
self.loaded_coords.append((x_center, y_center))
def get_n_labels(self):
return self.n_labels
def __len__(self):
return len(self.loaded_crops)
def __getitem__(self, idx):
crop = self.loaded_crops[idx]
x, y = self.loaded_coords[idx]
crop = self.transform(crop)
return crop, x, y
def get_image_path_without_coords(split: str or None = None):
coords_prefix_set = set()
for coords_name in os.listdir(COORDS_PATH):
coord_prefix = coords_name.split('_')[0]
coords_prefix_set.add(coord_prefix)
all_prefixes_set = set()
for tif_name in os.listdir(IMG_PATH):
coord_prefix = tif_name.split('_')[0]
all_prefixes_set.add(coord_prefix)
if split == Split.TRAIN:
missing_prefixes = coords_prefix_set
elif split == Split.TEST:
missing_prefixes = all_prefixes_set - coords_prefix_set
elif split is None:
missing_prefixes = all_prefixes_set
else:
raise ValueError
tif_filenames_list = []
labels_list = []
for prefix in missing_prefixes:
filename_matches = glob.glob(os.path.join(IMG_PATH, f'{prefix}_HAADF*NC*'))
if len(filename_matches) == 0:
continue
pos_filenames = [filename for filename in filename_matches if '_PtNC' in filename]
neg_filenames = [filename for filename in filename_matches if '_NC' in filename]
if len(pos_filenames) > 0:
pos_filename = sorted(pos_filenames)[-1]
tif_filenames_list.append(pos_filename)
labels_list.append(1)
if len(neg_filenames) > 0:
neg_filename = sorted(neg_filenames)[-1]
tif_filenames_list.append(neg_filename)
labels_list.append(0)
return tif_filenames_list, labels_list
if __name__ == "__main__":
filenames_list = get_image_path_without_coords()
filename = filenames_list[0]
dataset = SlidingCropDataset(filename)
|