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dataset.py
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import os
import glob
import scipy
import torch
import random
import numpy as np
import torchvision.transforms.functional as F
from PIL import Image
# from scipy.misc import imread
from imageio import imread
import cv2
from scipy.io import loadmat
import matplotlib.pyplot as plt
class Dataset_geno(torch.utils.data.Dataset):
def __init__(self, image_path, target_size, ori_size, dataset='', augment=True, training=True,
mask_reverse=False, creat_mask=False, reverse=False, assi=0, eval_train=False, scale=False, lowerb=0, ratio=0.2, Lratio=0, for_val=False):
super(Dataset_geno, self).__init__()
self.augment = augment
self.training = training
self.image_path = image_path
self.target_size = target_size
self.ori_size = ori_size
self.cut_ind = (self.target_size - self.ori_size) // 2
# self.use_ln = use_ln
# self.valuemask = valuemask
# self.multi_scale = multi_scale
# self.pair = pair
self.reverse = reverse
self.assi = assi
self.eval_train = eval_train
self.scale = scale
self.lowerb = lowerb
self.ratio = ratio
self.Lratio = Lratio
# self.joint = joint
# self.addnoise = addnoise
# self.ema = ema
self.for_val = for_val
# self.dataall = loadmat(image_path)
if self.training:
if loadmat(image_path)['test_genoMaps'].shape[-1] == 0:
# if 'test_genoMaps' not in loadmat(image_path).keys():
print('!!!!!!!!!!!!!!!!!!!!!!!!!')
self.dataall = loadmat(image_path)['train_genoMaps']
self.gtdataall = loadmat(image_path)['train_genoMaps_GT']
else:
self.dataall = np.concatenate([loadmat(image_path)['train_genoMaps'], loadmat(image_path)['test_genoMaps']], axis=-1)
self.gtdataall = np.concatenate([loadmat(image_path)['train_genoMaps_GT'], loadmat(image_path)['test_genoMaps_GT']], axis=-1)
# self.ratio_range = ratio_range
else:
if self.eval_train:
self.dataall = loadmat(image_path)['train_genoMaps']
self.gtdataall = loadmat(image_path)['train_genoMaps_GT']
else:
# self.dataall = loadmat(image_path)['train_genoMaps']
# self.gtdataall = loadmat(image_path)['train_genoMaps_GT']
if self.for_val:
self.dataall = loadmat(image_path)['test_genoMaps']
self.gtdataall = loadmat(image_path)['test_genoMaps_GT']
else:
# self.dataall = loadmat(image_path)['test_genoMaps']
# self.gtdataall = loadmat(image_path)['test_genoMaps_GT']
# if 'test_genoMaps' not in loadmat(image_path).keys():
if loadmat(image_path)['test_genoMaps'].shape[-1] == 0:
print('!!!!!!!!!!!!!!!!!!!!!!!!!')
self.dataall = loadmat(image_path)['train_genoMaps']
self.gtdataall = loadmat(image_path)['train_genoMaps_GT']
else:
self.dataall = np.concatenate([loadmat(image_path)['train_genoMaps'], loadmat(image_path)['test_genoMaps']], axis=-1)
self.gtdataall = np.concatenate([loadmat(image_path)['train_genoMaps_GT'], loadmat(image_path)['test_genoMaps_GT']], axis=-1)
self.maskall = (self.dataall > self.lowerb) * 1.0
# if self.use_ln:
# self.dataall = np.log(self.dataall * 10 + 1)
# # if not self.training:
# self.gtdataall = np.log(self.gtdataall * 10 + 1)
# self.maxall = self.dataall.max(0).max(0).max(0)
# else:
if not self.training:
self.maxall = self.dataall.max(0).max(0).max(0)
# self.dataall = self.dataall / self.dataall.max(0).max(0).max(0)
self.dataset = dataset
self.mask_reverse = mask_reverse
self.creat_mask = creat_mask
def __len__(self):
return self.dataall.shape[-1]
def __getitem__(self, index):
# try:
if not self.creat_mask:
if self.reverse:
item = self.load_item_reverse(index)
else:
item = self.load_item(index)
else:
if self.reverse:
item = self.load_create_item_valuemask_reverse(index)
else:
item = self.load_create_item_valuemask(index)
# except:
# print('loading error: ' + self.data[index])
# item = self.load_item(0)
return item
def padding_image(self, img, gt, mask):
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1])
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
img, gt, mask = imgnew, gtnew, masknew
return img, gt, mask
def load_item(self, index):
gt = self.gtdataall[..., index]
img = self.dataall[..., index]
img = np.where(img <= self.lowerb, 0, img)
if self.scale:
imgmax = img.max()
img = img / imgmax
mask = self.maskall[..., index]
maxnum = 1#self.maxall[index]
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1])
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
img, gt, mask = imgnew, gtnew, masknew
# augment data
# if self.training:
# if self.augment and np.random.binomial(1, 0.5) > 0:
# img = img[:, ::-1, ...]
# gt = gt[:, ::-1, ...]
# mask = mask[:, ::-1, ...]
# if self.augment and np.random.binomial(1, 0.5) > 0:
# img = img[::-1, ...]
# gt = gt[::-1, ...]
# mask = mask[::-1, ...]
#
# img = img / img.max()
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(gt.copy()).permute(2, 0, 1).float(), torch.tensor(maxnum)
def load_item_reverse(self, index):
gt = self.gtdataall[..., index]
img = self.dataall[..., index]
imgmax = img.max()
if self.scale:
img = img / imgmax
# assi = 0.5
img = np.where(img <= self.lowerb, self.assi, img)
mask = self.maskall[..., index]
maxnum = 1#self.maxall[index]
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1]) + self.assi
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
img, gt, mask = imgnew, gtnew, masknew
# augment data
# if self.training:
# if self.augment and np.random.binomial(1, 0.5) > 0:
# img = img[:, ::-1, ...]
# gt = gt[:, ::-1, ...]
# mask = mask[:, ::-1, ...]
# if self.augment and np.random.binomial(1, 0.5) > 0:
# img = img[::-1, ...]
# gt = gt[::-1, ...]
# mask = mask[::-1, ...]
#
# img = img / img.max()
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(gt.copy()).permute(2, 0, 1).float(), imgmax
def load_create_item_valuemask(self, index):
gt_real = self.gtdataall[..., index]
gt = self.dataall[..., index]
# gt = np.where(gt <= self.lowerb, 0, gt)
if self.scale:
gtmax = gt.max()
gt = gt / gt.max()
# findex = np.random.uniform(0, 1)
# if findex < 0.3:
# gt = gt[::-1].copy()
# elif findex < 0.6:
# gt = gt[:, ::-1].copy()
ratio2 = 1#np.random.uniform(1, 1.5, 1)[0]# np.random.uniform(1, 4, 1)[0] # np.random.uniform(1, 4, 1)[0]#1#np.random.uniform(4, 5, 1)[0]
gt = (gt * ratio2).clip(0, 1)
# gt = (gt * ratio2).clip(0, 1)
# scale = random.sample(np.arange(1, gt.max() * 0.6, 0.1).tolist(), 1)[0]
# idx = np.where(gt > 0)
idx1 = np.where((gt > self.lowerb))# & (gt < (0.9 * gt.max())))
# idx1 = np.where((gt >= gt.mean()))
# idx2 = np.where((gt < gt.mean()))
img = np.copy(gt)
# ratio = np.random.uniform(0.0, 0.2, 1)[0]
ratio1 = np.random.uniform(self.Lratio, self.ratio, 1)[0]#np.random.uniform(0.0, 0.7, 1)[0]
# ratio2 = np.random.uniform(0.5, 1., 1)[0]
pick2set0 = random.sample([i for i in range(len(idx1[0]))], int(len(idx1[0]) * ratio1))
pick2set0.sort()
indexnew = (idx1[0][pick2set0], idx1[1][pick2set0], idx1[2][pick2set0])
img[indexnew] = 0
if self.addnoise:
addindex = np.where(img > 0)
img[addindex] = np.random.normal(img.mean(), 0.05, len(addindex[0]))
img = img.clip(0, 1)
mask = np.where(gt > self.lowerb, 1, 0)
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1])
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
img, gt, mask = imgnew, gtnew, masknew
if self.scale:
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0,1).float(), torch.tensor(
gt.copy()).permute(2, 0, 1).float(), gtmax
else:
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(gt.copy()).permute(2, 0, 1).float()
def get_masked_img(self, img, gt, mask, idx):
ratio1 = np.random.uniform(self.Lratio, self.ratio, 1)[0] # np.random.uniform(0.0, 0.7, 1)[0]
pick2set0 = random.sample([i for i in range(len(idx[0]))], int(len(idx[0]) * ratio1))
pick2set0.sort()
indexnew = (idx[0][pick2set0], idx[1][pick2set0], idx[2][pick2set0])
img[indexnew] = self.assi
# mask = np.where(gt > self.lowerb, 1, 0)
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1])
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
img, gt, mask = imgnew, gtnew, masknew
return img, gt, mask
def load_create_item_valuemask_reverse(self, index):
# assi = 0.5#0#0.999
gt_real = self.gtdataall[..., index]
gt = self.dataall[..., index]
if self.scale:
gtmax = gt.max()
gt = gt / gt.max()
ratio2 = 1 # np.random.uniform(1, 1.5, 1)[0]# np.random.uniform(1, 4, 1)[0] # np.random.uniform(1, 4, 1)[0]#1#np.random.uniform(4, 5, 1)[0]
gt = (gt * ratio2).clip(0, 1)
# mask = np.where(gt > 0, 1, 0)
# gt_re = np.where(gt == 0, self.assi, gt)
mask = np.where(gt > self.lowerb, 1, 0)
gt_re = np.where(gt <= self.lowerb, self.assi, gt)
# idx = np.where((gt > 0))# & (gt < (0.9 * gt.max())))
idx = np.where((gt_re != self.assi))
img = np.copy(gt_re)
ratio = np.random.uniform(self.Lratio, self.ratio, 1)[0] # np.random.uniform(0.0, 0.7, 1)[0]
# ratio2 = np.random.uniform(0.5, 1., 1)[0]
pick2set0 = random.sample([i for i in range(len(idx[0]))], int(len(idx[0]) * ratio))
pick2set0.sort()
indexnew = (idx[0][pick2set0], idx[1][pick2set0], idx[2][pick2set0])
img[indexnew] = self.assi # 0
# if self.ema:
# img2 = np.copy(gt_re)
# ratio = np.random.uniform(self.Lratio, self.ratio, 1)[0]
# pick2set0 = random.sample([i for i in range(len(idx[0]))], int(len(idx[0]) * ratio))
# pick2set0.sort()
# indexnew = (idx[0][pick2set0], idx[1][pick2set0], idx[2][pick2set0])
# img2[indexnew] = self.assi # 0
if self.ori_size > self.target_size:
img = img[:self.target_size, :self.target_size, ...]
gt = gt[:self.target_size, :self.target_size, ...]
mask = mask[:self.target_size, :self.target_size, ...]
# if self.ema:
# img2 = img2[:self.target_size, :self.target_size, ...]
else:
imgnew = np.zeros([self.target_size, self.target_size, 1]) + self.assi
gtnew = np.zeros([self.target_size, self.target_size, 1])
masknew = np.ones([self.target_size, self.target_size, 1])
imgnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img
gtnew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = gt
masknew[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = mask
# if self.ema:
# imgnew2 = np.zeros([self.target_size, self.target_size, 1]) + self.assi
# imgnew2[self.cut_ind:self.cut_ind + self.ori_size, self.cut_ind:self.cut_ind + self.ori_size, :] = img2
# img2 = imgnew2
img, gt, mask = imgnew, gtnew, masknew
# if self.scale:
# return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(
# gt.copy()).permute(2, 0, 1).float(), gtmax
# else:
# if self.ema:
# return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(img2.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(gt.copy()).permute(2, 0, 1).float()
# else:
if self.for_val:
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0,
1).float(), torch.tensor(
gt.copy()).permute(2, 0, 1).float(), gt.max()
else:
return torch.tensor(img.copy()).permute(2, 0, 1).float(), torch.tensor(mask.copy()).permute(2, 0, 1).float(), torch.tensor(gt.copy()).permute(2, 0, 1).float()