def __getitem__(self, idx):
image = np.array(imread(self.images[idx]))
mask = np.array(imread(self.masks[idx], as_gray=True))
image = image[np.newaxis, ...]
mask = mask[np.newaxis, ...]
volumes = [image, mask]
image, mask = resize_sample(volumes, size=256)
image = normalize_volume(image)
mask = mask[..., np.newaxis]
image = np.squeeze(image, axis=0)
mask = np.squeeze(mask, axis=0)
image = image[..., 1]
image = image[..., np.newaxis]
image = np.concatenate((image, image, image), axis=2)
if self.transform is not None:
image, mask = self.transform((image, mask))
image = image.transpose(2, 0, 1)
mask = mask.transpose(2, 0, 1)
image_tensor = torch.from_numpy(image.astype(np.float32))
mask_tensor = torch.from_numpy(mask.astype(np.float32))
return image_tensor, mask_tensor
def __getitem__(self, idx):
image = np.array(imread(self.images[idx]))
mask = np.array(imread(self.masks[idx], as_gray=True))
unique = np.unique(mask)
if unique.shape[0] == 2:
self.image_true = image
self.mask_true = mask
mask = cv2.merge([mask, mask, mask])
image, mask = elastic_transform(image, mask, image.shape[1] * 3,
image.shape[1] * 0.08,
image.shape[1] * 0.08)
# image, mask = random_mask(image, mask)
elif unique.shape[0] == 1 and self.image_true is not None and (
self.count % 3) != 0:
image = self.image_true
mask = self.mask_true
mask = cv2.merge([mask, mask, mask])
image, mask = elastic_transform(image, mask, image.shape[1] * 3,
image.shape[1] * 0.08,
image.shape[1] * 0.08)
# image, mask = random_mask(image, mask)
self.count += 1
if self.count >= 30001:
self.count = 1
else:
self.count += 1
image = image[np.newaxis, ...]
mask = mask[np.newaxis, ...]
volumes = [image, mask]
volumes = resize_sample(volumes, size=256)
image, mask = volumes
image = normalize_volume(image)
mask = mask[..., np.newaxis]
image = np.squeeze(image, axis=0)
mask = np.squeeze(mask, axis=0)
image = image[..., 1]
image = image[..., np.newaxis]
image = np.concatenate((image, image, image), axis=2)
if self.transform is not None:
image, mask = self.transform((image, mask))
image = image.transpose(2, 0, 1)
mask = mask.transpose(2, 0, 1)
image_tensor = torch.from_numpy(image.astype(np.float32))
mask_tensor = torch.from_numpy(mask.astype(np.float32))
return image_tensor, mask_tensor
def __init__(
self,
images_dir,
transform=None,
image_size=256,
subset="train",
random_sampling=True,
validation_cases=0,
seed=42,
):
assert subset in ["all", "train", "validation"]
# read images
volumes = {}
masks = {}
print("reading {} images...".format(subset))
#dirpath 是当前目录， dirnames，是目录下的文件夹，filenames， 是目录下的文件
for (dirpath, dirnames, filenames) in os.walk(images_dir):
image_slices = []
mask_slices = []
mask_path = ""
#filter 来筛选名字带.tif的文件
#key指按照某一项排序
#filter 来筛选名字带.tif的文件
#key指按照某一项排序
for filename in sorted(filter(lambda f: ".gz" in f, filenames)):
if "seg" in filename:
mask_path = os.path.join(dirpath,filename)
mask_slices.append(load_nii(mask_path))
else:
filepath = os.path.join(dirpath, filename)
image_slices.append(load_nii(filepath))
embed()
#只筛选带有肿瘤的slice
if len(image_slices) > 0:
patient_id = dirpath.split("/")[-1]
volumes[patient_id] = np.array(image_slices).transpose(1,2,3,0)
masks[patient_id] = np.array(mask_slices).transpose(1,2,3,0)
embed()
#patient 是一个字典，里面是patient_id和其对应的image(无mask)
self.patients = sorted(volumes)
# select cases to subset
if not subset == "all":
random.seed(seed)
#分出validation set
validation_patients = random.sample(self.patients, k=validation_cases) #注意K有可能超
if subset == "validation":
self.patients = validation_patients
else:
self.patients = sorted(
list(set(self.patients).difference(validation_patients))
)
print("preprocessing {} volumes...".format(subset))
# create list of tuples (volume, mask)
self.volumes = [(volumes[k], masks[k]) for k in self.patients]
embed()
# probabilities for sampling slices based on masks
self.slice_weights = [m.sum(axis=-1).sum(axis=-1).sum(axis=-1) for v, m in self.volumes]
self.slice_weights = [(s + (s.sum() * 0.1 / len(s))) / (s.sum() * 1.1) for s in self.slice_weights]
print("one hotting {} masks...".format(subset))
self.volumes = [(v, make_one_hot(m)) for v, m in self.volumes]
embed()
print("resizing {} volumes...".format(subset))
# resize
self.volumes = [resize_sample(v, size=image_size) for v in self.volumes]
embed()
print("normalizing {} volumes...".format(subset))
# normalize channel-wise
self.volumes = [(normalize_volume(v), m) for v, m in self.volumes]
embed()
print("one hotting {} masks...".format(subset))
self.volumes = [(v, convert_mask_to_one(m)) for v, m in self.volumes]
embed()
print("done creating {} dataset".format(subset))
# create global index for patient and slice (idx -> (p_idx, s_idx))
num_slices = [v.shape[0] for v, m in self.volumes]
self.patient_slice_index = list(
zip(
sum([[i] * num_slices[i] for i in range(len(num_slices))], []),
sum([list(range(x)) for x in num_slices], []),
)
)
self.random_sampling = random_sampling
self.transform = transform
embed()
def __init__(
self,
images_dir,
transform=None,
image_size=256,
subset="train",
random_sampling=True,
validation_cases=10,
seed=42,
):
assert subset in ["all", "train", "validation"]
# read images
volumes = {}
masks = {}
print("reading {} images...".format(subset))
for (dirpath, dirnames, filenames) in os.walk(images_dir):
image_slices = []
mask_slices = []
for filename in sorted(
filter(lambda f: ".tif" in f, filenames),
key=lambda x: int(x.split(".")[-2].split("_")[4]),
):
filepath = os.path.join(dirpath, filename)
if "mask" in filename:
mask_slices.append(imread(filepath, as_gray=True))
else:
image_slices.append(imread(filepath))
if len(image_slices) > 0:
patient_id = dirpath.split("/")[-1]
volumes[patient_id] = np.array(image_slices[1:-1])
masks[patient_id] = np.array(mask_slices[1:-1])
self.patients = sorted(volumes)
# select cases to subset
if not subset == "all":
random.seed(seed)
validation_patients = random.sample(self.patients,
k=validation_cases)
if subset == "validation":
self.patients = validation_patients
else:
self.patients = sorted(
list(set(self.patients).difference(validation_patients)))
print("preprocessing {} volumes...".format(subset))
# create list of tuples (volume, mask)
self.volumes = [(volumes[k], masks[k]) for k in self.patients]
print("cropping {} volumes...".format(subset))
# crop to smallest enclosing volume
self.volumes = [crop_sample(v) for v in self.volumes]
print("padding {} volumes...".format(subset))
# pad to square
self.volumes = [pad_sample(v) for v in self.volumes]
print("resizing {} volumes...".format(subset))
# resize
self.volumes = [
resize_sample(v, size=image_size) for v in self.volumes
]
print("normalizing {} volumes...".format(subset))
# normalize channel-wise
self.volumes = [(normalize_volume(v), m) for v, m in self.volumes]
# probabilities for sampling slices based on masks
self.slice_weights = [
m.sum(axis=-1).sum(axis=-1) for v, m in self.volumes
]
self.slice_weights = [(s + (s.sum() * 0.1 / len(s))) / (s.sum() * 1.1)
for s in self.slice_weights]
# add channel dimension to masks
self.volumes = [(v, m[..., np.newaxis]) for (v, m) in self.volumes]
print("done creating {} dataset".format(subset))
# create global index for patient and slice (idx -> (p_idx, s_idx))
num_slices = [v.shape[0] for v, m in self.volumes]
self.patient_slice_index = list(
zip(
sum([[i] * num_slices[i] for i in range(len(num_slices))], []),
sum([list(range(x)) for x in num_slices], []),
))
self.random_sampling = random_sampling
self.transform = transform