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e5ccff5 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 | from torch.utils.data import Dataset
import cv2
import numpy as np
import os
import pandas as pd
from sklearn.model_selection import train_test_split
import logging
from PIL import Image
from .data_path import get_dataset_path
## JSRT downloaded from: https://github.com/AnishHota/JSRT-Segmentation
# One fold contained all 124 odd numbered images in the JSRT database. The other fold contained the 123 even numbered images.
class JSRT(Dataset):
def __init__(self, split='train', transform=None, mode='heart', dataset_seed=42):
super().__init__()
self.split = split
self.root = get_dataset_path('JSRT')
self.transform = transform
self.mode = mode
train_list, test_list = [], []
for p in os.listdir(os.path.join(self.root, 'images')):
ind = int(p.replace('JPCLN', '').replace('JPCNN', '').split('.')[0])
if ind % 2 != 0:
train_list.append(p)
else:
test_list.append(p)
train_list, val_list = train_test_split(train_list, train_size=0.9, random_state=dataset_seed)
logging.info(f'JSRT Dataset Seed: {dataset_seed}')
self.data_list = dict(train=train_list, val=val_list, test=test_list)[split]
self.num_classes = 1
self.mode = mode
assert mode in ('heart', 'lung', 'clavicle')
logging.info(f'JSRT Mode={mode}, split={split}, #data={len(self.data_list)}')
def __getitem__(self, index):
p = self.data_list[index]
image = cv2.imread(os.path.join(self.root, f'images', p))
image = cv2.resize(image, (1024, 1024))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if self.mode == 'heart':
mask = cv2.imread(os.path.join(self.root, 'masks', 'heart', p.replace('.jpg', '.png')))[:, :, 1]
mask[mask > 0]=1
elif self.mode == 'clavicle':
mask1 = cv2.imread(os.path.join(self.root, 'masks', 'left_clavicle', p.replace('.jpg', '.png')))[:, :, 1]
mask1[mask1 > 0]=1
mask2 = cv2.imread(os.path.join(self.root, 'masks', 'right_clavicle', p.replace('.jpg', '.png')))[:, :, 1]
mask2[mask2 > 0]=1
mask = np.logical_or(mask1, mask2).astype(int)
elif self.mode == 'lung':
mask1 = cv2.imread(os.path.join(self.root, 'masks', 'left_lung', p.replace('.jpg', '.png')))[:, :, 1]
mask1[mask1 > 0]=1
mask2 = cv2.imread(os.path.join(self.root, 'masks', 'right_lung', p.replace('.jpg', '.png')))[:, :, 1]
mask2[mask2 > 0]=1
mask = np.logical_or(mask1, mask2).astype(int)
else:
raise NotImplementedError
transformed = self.transform(image=image, mask=mask)
image = transformed['image']
mask = transformed['mask'].unsqueeze(0)
return image.float(), mask.float()
def __len__(self):
return len(self.data_list)
import os
import torch
from skimage import io, transform
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
import cv2
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from skimage.transform import resize
class JSRT_Landmark(Dataset):
def __init__(self, split='train'):
self.split = split
self.root = get_dataset_path('JSRT_jpg')
train_list, test_list = [], []
landmark_path = 'annotations/landmarks_jsrt'
train_landmarks = []
test_landmarks = []
for p in os.listdir(landmark_path + '/H'):
ind = int(p.replace('JPCLN', '').replace('JPCNN', '').split('.')[0])
h = np.load(os.path.join(landmark_path, 'H', p)).astype('float')
ll = np.load(os.path.join(landmark_path, 'LL', p)).astype(np.float32)
rl = np.load(os.path.join(landmark_path, 'RL', p)).astype(np.float32)
landmarks = np.concatenate([rl, ll, h], axis = 0)
if ind % 2 != 0:
train_list.append(p.replace('.npy', '.jpg'))
train_landmarks.append(landmarks)
else:
test_list.append(p.replace('.npy', '.jpg'))
test_landmarks.append(landmarks)
self.data_list = dict(train=train_list, val=test_list, test=test_list)[split]
self.landmarks = dict(train=train_landmarks, val=test_landmarks, test=test_landmarks)[split]
self.num_classes = 240
if split == 'train':
self.transform = transforms.Compose([
# RandomScale(),
# Rotate(3),
# AugColor(0.40),
ToTensor(),
])
else:
self.transform = ToTensor()
def __len__(self):
return len(self.data_list)
def __getitem__(self, idx):
img_name = self.data_list[idx]
img_path = os.path.join(self.root, img_name)
image = io.imread(img_path).astype('float') / 255.0
# image = resize(image, output_shape=(1024, 1024, 3))
# image = np.expand_dims(image, axis=2)
# print(image.shape)
landmarks = self.landmarks[idx].copy()
sample = {'image': image, 'landmarks': landmarks}
if self.transform:
sample = self.transform(sample)
return sample['image'], sample['landmarks']
class RandomScale(object):
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
# Pongo limites para evitar que los landmarks salgan del contorno
min_x = np.min(landmarks[:,0])
max_x = np.max(landmarks[:,0])
ancho = max_x - min_x
min_y = np.min(landmarks[:,1])
max_y = np.max(landmarks[:,1])
alto = max_y - min_y
max_var_x = 1024 / ancho
max_var_y = 1024 / alto
min_var_x = 0.80
min_var_y = 0.80
varx = np.random.uniform(min_var_x, max_var_x)
vary = np.random.uniform(min_var_y, max_var_y)
landmarks[:,0] = landmarks[:,0] * varx
landmarks[:,1] = landmarks[:,1] * vary
h, w = image.shape[:2]
new_h = np.round(h * vary).astype('int')
new_w = np.round(w * varx).astype('int')
img = transform.resize(image, (new_h, new_w))
# Cropeo o padeo aleatoriamente
min_x = np.round(np.min(landmarks[:,0])).astype('int')
max_x = np.round(np.max(landmarks[:,0])).astype('int')
min_y = np.round(np.min(landmarks[:,1])).astype('int')
max_y = np.round(np.max(landmarks[:,1])).astype('int')
if new_h > 1024:
rango = 1024 - (max_y - min_y)
maxl0y = new_h - 1025
if rango > 0 and min_y > 0:
l0y = min_y - np.random.randint(0, min(rango, min_y))
l0y = min(maxl0y, l0y)
else:
l0y = min_y
l1y = l0y + 1024
img = img[l0y:l1y,:]
landmarks[:,1] -= l0y
elif new_h < 1024:
pad = h - new_h
p0 = np.random.randint(np.floor(pad/4), np.ceil(3*pad/4))
p1 = pad - p0
img = np.pad(img, ((p0, p1), (0, 0), (0, 0)), mode='constant', constant_values=0)
landmarks[:,1] += p0
if new_w > 1024:
rango = 1024 - (max_x - min_x)
maxl0x = new_w - 1025
if rango > 0 and min_x > 0:
l0x = min_x - np.random.randint(0, min(rango, min_x))
l0x = min(maxl0x, l0x)
else:
l0x = min_x
l1x = l0x + 1024
img = img[:, l0x:l1x]
landmarks[:,0] -= l0x
elif new_w < 1024:
pad = w - new_w
p0 = np.random.randint(np.floor(pad/4), np.ceil(3*pad/4))
p1 = pad - p0
img = np.pad(img, ((0, 0), (p0, p1), (0, 0)), mode='constant', constant_values=0)
landmarks[:,0] += p0
if img.shape[0] != 1024 or img.shape[1] != 1024:
print('Original', [new_h,new_w])
print('Salida', img.shape)
raise Exception('Error')
return {'image': img, 'landmarks': landmarks}
def adjust_gamma(image, gamma=1.0):
# build a lookup table mapping the pixel values [0, 255] to
# their adjusted gamma values
invGamma = 1.0 / gamma
table = np.array([((i / 255.0) ** invGamma) * 255
for i in np.arange(0, 256)]).astype("uint8")
# apply gamma correction using the lookup table
return np.float32(cv2.LUT(image.astype('uint8'), table))
class AugColor(object):
def __init__(self, gammaFactor):
self.gammaf = gammaFactor
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
# Gamma
gamma = np.random.uniform(1 - self.gammaf, 1 + self.gammaf / 2)
image[:,:,0] = adjust_gamma(image[:,:,0] * 255, gamma) / 255
# Adds a little noise
image = image + np.random.normal(0, 1/128, image.shape)
return {'image': image, 'landmarks': landmarks}
class Rotate(object):
def __init__(self, angle):
self.angle = angle
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
angle = np.random.uniform(- self.angle, self.angle)
image = transform.rotate(image, angle)
centro = image.shape[0] / 2, image.shape[1] / 2
landmarks -= centro
theta = np.deg2rad(angle)
c, s = np.cos(theta), np.sin(theta)
R = np.array(((c, -s), (s, c)))
landmarks = np.dot(landmarks, R)
landmarks += centro
return {'image': image, 'landmarks': landmarks}
import torchvision.transforms.functional
import torch.nn.functional as F
class ToTensor(object):
"""Convert ndarrays in sample to Tensors."""
def __call__(self, sample):
image, landmarks = sample['image'], sample['landmarks']
# swap color axis because
# numpy image: H x W x C
# torch image: C X H X W
size = image.shape[0]
image = image.transpose((2, 0, 1))
landmarks = landmarks / size
landmarks = np.clip(landmarks, 0, 1)
image = torch.from_numpy(image).float()
image = transforms.functional.normalize(image, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD)
return {'image': image,
'landmarks': torch.from_numpy(landmarks).float()} |