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import logging
import os
import numpy as np
import torch
import cv2
from skimage.transform import resize
from torch.utils.data import Dataset
class SegmentationDataset_train(Dataset):
def __init__(self, nonlabel_path: str, havelabel_path: str, dataset: str, scale = (224, 224)):
self.nonlabel_path = nonlabel_path
self.havelabel_path = havelabel_path
self.name_dataset = dataset
self.scale = scale
with open(self.nonlabel_path, 'r') as nlf:
lines = nlf.readlines()
non_label_lines = [line.strip().split(' ')[:2] for line in lines]
with open(self.havelabel_path, 'r') as hlf:
lines = hlf.readlines()
have_label_lines = [line.strip().split(' ')[:2] for line in lines]
if len(non_label_lines) == 0:
self.ids = np.array(have_label_lines, dtype= object)
else:
choose_non_lable_lines = np.random.choice(len(non_label_lines), size = len(have_label_lines))
non_label_lines = np.array(non_label_lines, dtype= object)
have_label_lines = np.array(have_label_lines, dtype= object)
self.ids = np.concatenate([non_label_lines[choose_non_lable_lines], have_label_lines], axis= 0)
# self.ids = os.listdir(images_dir) #[splitext(file)[0] for file in listdir(images_dir) if not file.startswith('.') and image_type in file]
# print(len(self.ids))
# if datasetname == "las_mri":
# self.ids = [f for f in self.ids if image_type in f]
if len(self.ids) == 0:
raise RuntimeError(f'No input file found in {self.images_dir}, make sure you put your images there')
logging.info(f'Creating dataset with {len(self.ids)} examples')
self.cache = {}
def __len__(self):
return len(self.ids)
@classmethod
def preprocess(self, img, scale, is_mask):
img = resize(img,
(scale[0], scale[0]),
order=0,
preserve_range=True,
anti_aliasing=False).astype('uint8')
img = np.asarray(img)
if not is_mask:
img = ((img - img.min()) * (1/(0.01 + img.max() - img.min()) * 255)).astype('uint8')
if len(img.shape) != 3:
img = np.expand_dims(img, axis=2) #(1, 224, 224)
if is_mask:
img = resize(img,
(scale[1], scale[1]),
order=0,
preserve_range=True,
anti_aliasing=False).astype('uint8')
return img
@classmethod
def load(self, filename, name_dataset, is_mask=False):
if name_dataset.startswith("las"):
if is_mask:
return cv2.imread(filename, cv2.IMREAD_UNCHANGED)
else:
img = cv2.imread(filename, 0)
return img
else:
if is_mask:
return cv2.imread(filename, 0)
else:
return cv2.imread(filename)
def __getitem__(self, idx):
if idx in self.cache:
return self.cache[idx]
img_file = self.ids[idx][0]
mask_file = self.ids[idx][1]
# print(img_file)
#start = time.time()
mask = self.load(mask_file, self.name_dataset, is_mask=True)
img = self.load(img_file, self.name_dataset, is_mask=False)
assert mask is not None, mask_file
assert img is not None, img_file
if self.name_dataset in ["kvasir", "buidnewprocess"]:
mask[mask < 50] = 0
mask[mask > 200] = 1
elif self.name_dataset == "isiconlytrain":
mask[mask > 1] = 1
elif self.name_dataset.startswith("las"):
mask[mask == 30] = 1
mask[mask == 60] = 2 # main predict
mask[mask == 90] = 3
mask[mask == 120] = 4
mask[mask == 150] = 5
mask[mask == 180] = 6
mask[mask == 210] = 7
mask[mask > 7] = 0
else:
mask[mask>0] = 1
img = self.preprocess(img, self.scale, is_mask=False)
mask = self.preprocess(mask, self.scale, is_mask=True)
data = {
'image': torch.as_tensor(img.copy()).permute(2, 0, 1).float().contiguous(),
'mask_ete': torch.as_tensor(mask.copy().astype(int)).long().contiguous(),
'mask_file' : mask_file,
'img_file' : img_file
}
self.cache[idx] = data
return data
def get_3d_iter(self):
from itertools import groupby
keyf = lambda idx : self.ids[idx].split("_frame_")[0]
sorted_ids = sorted(range(len(self.ids)), key=lambda i : self.ids[i])
for _, items in groupby(sorted_ids, key=keyf):
images = []
masks_ete = []
for idx in items:
d = self.__getitem__(idx)
images.append(d['image'])
masks_ete.append(d['mask_ete'])
# store third dimension in image channels
images = torch.stack(images, dim=0)
masks_ete = torch.stack(masks_ete, dim=0)
_3d_data = {'image': images, 'mask_ete': masks_ete}
yield _3d_data
class SegmentationDataset(Dataset):
def __init__(self, name_dataset: str, images_dir: str, masks_dir: str, scale = (1024, 256)):
self.images_dir = images_dir
self.masks_dir = masks_dir
self.scale = scale
self.name_dataset = name_dataset
self.ids = os.listdir(images_dir)
if len(self.ids) == 0:
raise RuntimeError(f'No input file found in {self.images_dir}, make sure you put your images there')
logging.info(f'Creating dataset with {len(self.ids)} examples')
self.cache = {}
def __len__(self):
return len(self.ids)
@classmethod
def preprocess(self, img, scale, is_mask):
img = resize(img,
(scale[0], scale[0]),
order=0,
preserve_range=True,
anti_aliasing=False).astype('uint8')
img = np.asarray(img)
if not is_mask:
img = ((img - img.min()) * (1/(img.max() - img.min()) * 255)).astype('uint8')
if len(img.shape) != 3:
img = np.expand_dims(img, axis=2) #(1, 224, 224)
if is_mask:
img = resize(img,
(scale[1], scale[1]),
order=0,
preserve_range=True,
anti_aliasing=False).astype('uint8')
return img
@classmethod
def load(self, filename, name_dataset, is_mask=False):
if name_dataset.startswith("las"):
if is_mask:
return cv2.imread(filename, cv2.IMREAD_UNCHANGED)
else:
img = cv2.imread(filename, 0)
return img
else:
if is_mask:
return cv2.imread(filename, 0)
else:
return cv2.imread(filename)
def __getitem__(self, idx):
if idx in self.cache:
return self.cache[idx]
name = self.ids[idx]
if self.name_dataset == "isiconlytrain":
mask_file = os.path.join(self.masks_dir, name).split(".jpg")[0]
mask_file = mask_file + "_segmentation.png"
elif self.name_dataset == "buidnewprocess":
mask_file = os.path.join(self.masks_dir, name)
elif self.name_dataset == "kvasir":
mask_file = os.path.join(self.masks_dir, name)
elif self.name_dataset == "drive":
mask_file = os.path.join(self.masks_dir, name).replace("training", "manual1")
elif self.name_dataset == "bts":
mask_file = os.path.join(self.masks_dir, name).replace(self.image_type, "_seg_")
elif self.name_dataset in ["las_mri", "las_ct"]:
mask_file = os.path.join(self.masks_dir, name).replace("image", "label")
else:
mask_file = os.path.join(self.masks_dir, name)
img_file = os.path.join(self.images_dir, name)
mask = self.load(mask_file, self.name_dataset, is_mask=True)
img = self.load(img_file, self.name_dataset, is_mask=False)
assert mask is not None, mask_file
assert img is not None, img_file
if self.name_dataset in ["kvasir", "buidnewprocess"]:
mask[mask < 50] = 0
mask[mask > 200] = 1
elif self.name_dataset == "isiconlytrain":
mask[mask > 1] = 1
elif self.name_dataset.startswith("las"):
mask[mask == 30] = 1
mask[mask == 60] = 2 # main predict
mask[mask == 90] = 3
mask[mask == 120] = 4
mask[mask == 150] = 5
mask[mask == 180] = 6
mask[mask == 210] = 7
mask[mask > 7] = 0
else:
mask[mask>0] = 1
img = self.preprocess(img, self.scale, is_mask=False)
mask = self.preprocess(mask, self.scale, is_mask=True)
data = {
'image': torch.as_tensor(img.copy()).permute(2, 0, 1).float().contiguous(),
'mask_ete': torch.as_tensor(mask.copy().astype(int)).long().contiguous(),
'mask_file' : mask_file,
'img_file' : img_file
}
self.cache[idx] = data
return data
def get_3d_iter(self):
from itertools import groupby
keyf = lambda idx : self.ids[idx].split("_frame_")[0]
sorted_ids = sorted(range(len(self.ids)), key=lambda i : self.ids[i])
for _, items in groupby(sorted_ids, key=keyf):
images = []
masks_ete = []
for idx in items:
d = self.__getitem__(idx)
images.append(d['image'])
masks_ete.append(d['mask_ete'])
# store third dimension in image channels
images = torch.stack(images, dim=0)
masks_ete = torch.stack(masks_ete, dim=0)
_3d_data = {'image': images, 'mask_ete': masks_ete}
yield _3d_data
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