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ZY3LC_loader.py
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ZY3LC_loader.py
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'''
2021.4.14 load ZY3LC
'''
import torch.utils.data as data
import tifffile as tif
import albumentations as A
import torch
import numpy as np
import cv2
import os
IMG_EXTENSIONS = [
'.jpg', '.JPG', '.jpeg', '.JPEG',
'.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP',
]
# for classification
image_transformed_cls = A.Compose([
A.Flip(p=0.5),
A.RandomGridShuffle(grid=(2, 2), p=0.5),
A.Rotate(p=0.5),
# A.RandomBrightnessContrast(p=0.5), #wierd, for augcolor, have tested on 2022.2.15,should delete
]
)
# img_mean and std
# 2021.7.1: update mux images and add tlc
IMG_MEAN_ALL = [1142.05719085069, 1183.92746808790, 1324.37698042479, 2360.08189621090]
IMG_STD_ALL = [352.892230743533, 402.069966221899, 554.259982955950, 1096.14879868840]
TLC_MEAN_ALL = [440.312064755891, 387.339043098102, 444.801891941169]
TLC_STD_ALL = [270.286351619591, 202.061888100090, 216.688621196791]
# For all cities used for training
class myImageFloder(data.Dataset):
def __init__(self, left, cls, imgsize = 512, channels=4, aug=False):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = (left_img - IMG_MEAN_ALL[:self.channels]) / IMG_STD_ALL[:self.channels]
left_img = torch.from_numpy(left_img).permute(2, 0, 1).float() # H W C ==> C H W
# classification
lab = cls_img-1 # [0, C-1]
lab = torch.from_numpy(lab).long()
return left_img, lab
def __len__(self):
return len(self.left)
# For all cities used for training
class myImageFloder_binary(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, positive=0):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = (left_img - IMG_MEAN_ALL[:self.channels]) / IMG_STD_ALL[:self.channels]
left_img = torch.from_numpy(left_img).permute(2, 0, 1).float() # H W C ==> C H W
# classification
lab = np.zeros_like(cls_img, dtype=cls_img.dtype)
lab[cls_img==self.positive] = 1
return left_img, lab
def __len__(self):
return len(self.left)
# For all cities used for training
class myImageFloder_binarypath(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, positive=0, num_sample=0):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
if num_sample > 0:
self.left = left[:num_sample]
self.cls = cls[:num_sample]
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = (left_img - IMG_MEAN_ALL[:self.channels]) / IMG_STD_ALL[:self.channels]
left_img = torch.from_numpy(left_img).permute(2, 0, 1).float() # H W C ==> C H W
# classification
lab = np.zeros_like(cls_img, dtype=cls_img.dtype)
lab[cls_img==self.positive] = 1
return left_img, lab, left
def __len__(self):
return len(self.left)
# 2022.01.19
# for 8 bit images
class myImageFloder_8bit(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
cls_img = cls_img-1 # [0, C-1]
cls_img = torch.from_numpy(cls_img).long()
return left_img, cls_img
def __len__(self):
return len(self.left)
# for binary
class myImageFloder_8bit_binary(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, positive=0,
iscrop=False, istlc=False, ismabi=False):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
self.iscrop = iscrop
self.istlc = istlc
self.ismabi = ismabi
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
if self.istlc:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
tlc = tif.imread(os.path.join(idir, 'tlc', 'tlc' + iname[3:]))
left_img = np.concatenate((left_img, tlc), axis=2) # H W C
if self.ismabi:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
mabi = cv2.imread(os.path.join(idir, 'mabi', 'mabi' + iname[3:-4]+'.png'),
cv2.IMREAD_UNCHANGED) # 0-255, H W
mabi = np.expand_dims(mabi, axis=2) # H W to H W 1
left_img = np.concatenate((left_img, mabi), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
if self.iscrop:
left_img = A.center_crop(left_img, self.imgsize, self.imgsize)
cls_img = A.center_crop(cls_img, self.imgsize, self.imgsize)
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
lab = np.zeros_like(cls_img, dtype=cls_img.dtype)
lab[cls_img==self.positive] = 1
lab = torch.from_numpy(lab).long()
return left_img, lab
def __len__(self):
return len(self.left)
# for binary
class myImageFloder_8bit_binarypath(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, positive=0,num_sample=0,
iscrop=False, istlc=False, ismabi=False):
self.left = left
self.cls = cls
if num_sample > 0:
self.left = left[:num_sample]
self.cls = cls[:num_sample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
self.iscrop = iscrop
self.istlc = istlc
self.ismabi = ismabi
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
if self.istlc:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
tlc = tif.imread(os.path.join(idir, 'tlc', 'tlc' + iname[3:]))
left_img = np.concatenate((left_img, tlc), axis=2) # H W C
if self.ismabi:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
mabi = cv2.imread(os.path.join(idir, 'mabi', 'mabi' + iname[3:-4]+'.png'),
cv2.IMREAD_UNCHANGED) # 0-255, H W
mabi = np.expand_dims(mabi, axis=2) # H W to H W 1
left_img = np.concatenate((left_img, mabi), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
if self.iscrop:
left_img = A.center_crop(left_img, self.imgsize, self.imgsize)
cls_img = A.center_crop(cls_img, self.imgsize, self.imgsize)
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
lab = np.zeros_like(cls_img, dtype=cls_img.dtype)
lab[cls_img==self.positive] = 1
lab = torch.from_numpy(lab).long()
return left_img, lab, left
def __len__(self):
return len(self.left)
# 2022.9.18: only return img
class myImageFloder_8bit_binarypath_img(data.Dataset):
def __init__(self, left, channels=4):
self.left = left
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
return left_img, left
def __len__(self):
return len(self.left)
# for binary, return img and path
class myImageFloder_8bit_binarypath_gid(data.Dataset):
def __init__(self, left, imgsize = 256, channels=4, aug=False, positive=0, num_sample=0):
self.left = left
if num_sample > 0:
self.left = left[:num_sample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
def __getitem__(self, index):
left = self.left[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img)
left_img = transformed["image"]
# scale images
left_img = left_img[:,:,::-1].copy() # nir-rgb to bgr-nir
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
return left_img, left
def __len__(self):
return len(self.left)
# for binary update
class myImageFloder_8bit_binary_update(data.Dataset):
def __init__(self, left, cls, updatepath, imgsize = 256, channels=4, aug=False,
positive=0, returnpath=False, istlc = False, ismabi=False):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
self.updatepath = updatepath
self.returnpath = returnpath # add
self.istlc = istlc
self.ismabi = ismabi
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# base name
ibase = os.path.basename(left)[:-4]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
update = cv2.imread(os.path.join(self.updatepath, ibase + '_up.png'), cv2.IMREAD_UNCHANGED)
# add
if self.istlc:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
tlc = tif.imread(os.path.join(idir, 'tlc', 'tlc' + iname[3:]))
left_img = np.concatenate((left_img, tlc), axis=2) # H W C
if self.ismabi:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
mabi = cv2.imread(os.path.join(idir, 'mabi', 'mabi' + iname[3:-4]+'.png'),
cv2.IMREAD_UNCHANGED) # 0-255, H W
mabi = np.expand_dims(mabi, axis=2) # H W to H W 1
left_img = np.concatenate((left_img, mabi), axis=2) # H W C
ref = np.stack((cls_img, update), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=ref)
left_img = transformed["image"]
ref = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# lab and update
cls_img = ref[:, :, 0]
lab = np.zeros_like(cls_img, dtype=cls_img.dtype)
lab[cls_img==self.positive] = 1
lab = torch.from_numpy(lab).long()
update = torch.from_numpy(ref[:, :, 1]).long()
if self.returnpath:
return left_img, lab, update, left
else:
return left_img, lab, update
def __len__(self):
return len(self.left)
# for binary update then scratch
class myImageFloder_8bit_binary_update_scratch(data.Dataset):
def __init__(self, left, updatepath, imgsize = 256, channels=4,
aug=False, positive=0, ismabi=False):
self.left = left
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.positive = positive
self.updatepath = updatepath
self.ismabi = ismabi
def __getitem__(self, index):
left = self.left[index]
# base name
ibase = os.path.basename(left)[:-4]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
update = cv2.imread(os.path.join(self.updatepath, ibase + '_up.png'), cv2.IMREAD_UNCHANGED)
if self.ismabi:
iname = os.path.basename(left)
idir = os.path.dirname(os.path.dirname(left))
mabi = cv2.imread(os.path.join(idir, 'mabi', 'mabi' + iname[3:-4]+'.png'),
cv2.IMREAD_UNCHANGED) # 0-255, H W
mabi = np.expand_dims(mabi, axis=2) # H W to H W 1
left_img = np.concatenate((left_img, mabi), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=update)
left_img = transformed["image"]
update = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# updated labels
update = torch.from_numpy(update).long() # 0, 1
return left_img, update
def __len__(self):
return len(self.left)
# for 8 bit images
class myImageFloder_8bitpath(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, num_sample=0):
self.left = left
self.cls = cls
if num_sample > 0:
self.left = left[:num_sample]
self.cls = cls[:num_sample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
cls_img = cls_img-1 # [0, C-1]
cls_img = torch.from_numpy(cls_img).long()
return left_img, cls_img, left
def __len__(self):
return len(self.left)
# For all cities used for training
class myImageFloder_update(data.Dataset):
def __init__(self, left, cls, cls_update, imgsize = 512, channels=4, aug=False):
self.left = left
self.cls = cls
self.cls_update = cls_update
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
cls_update = self.cls_update[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img_old = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
cls_img_update = cv2.imread(cls_update, cv2.IMREAD_UNCHANGED) # PNG
cls_img = np.stack((cls_img_old, cls_img_update), axis=2) # w h 2: noise, update
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = (left_img - IMG_MEAN_ALL[:self.channels]) / IMG_STD_ALL[:self.channels]
left_img = torch.from_numpy(left_img).permute(2, 0, 1).float() # H W C ==> C H W
# classification
cls_img = cls_img-1 # [0, C-1]
cls_img = torch.from_numpy(cls_img).long()
return left_img, cls_img[:,:,0], cls_img[:,:,1], os.path.basename(cls)
def __len__(self):
return len(self.left)
# For all cities used for eval train folder
# return img, lab, respath
class myImageFloder_evaltrain(data.Dataset):
def __init__(self, left, cls, imgsize = 512, channels=4, aug=False):
self.left = left
self.cls = cls
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = (left_img - IMG_MEAN_ALL[:self.channels]) / IMG_STD_ALL[:self.channels]
left_img = torch.from_numpy(left_img).permute(2, 0, 1).float() # H W C ==> C H W
# classification
lab = cls_img-1 # [0, C-1]
lab = torch.from_numpy(lab).long()
return left_img, lab, os.path.basename(cls)
def __len__(self):
return len(self.left)
# for change
class myImageFloder_8bit_t1t2(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, returnpath=False,
numsample=0, positive=0, dir1='img1', dir2='img2', isprob=False):
self.left = left
self.cls = cls
if numsample>0:
self.left = left[:numsample]
self.cls = cls[:numsample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.returnpath = returnpath
self.positive = positive
self.dir1 = dir1
self.dir2 = dir2
self.isprob = isprob
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
idir = os.path.dirname(os.path.dirname(left))
iname = os.path.basename(left)
left = os.path.join(idir, self.dir1, iname)
left2 = os.path.join(idir, self.dir2, iname)
# Read images
left_img1 = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
left_img2 = tif.imread(left2)[:, :, :self.channels] # TIF
left_img = np.concatenate((left_img1, left_img2), axis=2) # H W C
# add change probability
if self.isprob:
probp = os.path.join(idir, 'prob', 'prob'+iname[3:-4]+'.png')
prob = cv2.imread(probp, cv2.IMREAD_UNCHANGED)
prob = np.expand_dims(prob, axis=2) # H W 1
left_img = np.concatenate((left_img, prob), axis=2)
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
if self.positive>0:
cls_img = (cls_img==self.positive) # pos: 1, neg: 0
cls_img = torch.from_numpy(cls_img).long()
if not self.returnpath:
return left_img, cls_img
else:
return left_img, cls_img, left
def __len__(self):
return len(self.left)
# for change
class myImageFloder_8bit_t1t1(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, returnpath=False,
numsample=0):
self.left = left
self.cls = cls
if numsample>0:
self.left = left[:numsample]
self.cls = cls[:numsample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.returnpath = returnpath
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
left_img1 = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
left_img = np.concatenate((left_img1, left_img1), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
cls_img = torch.from_numpy(cls_img).long()
if not self.returnpath:
return left_img, cls_img
else:
return left_img, cls_img, left
def __len__(self):
return len(self.left)
# for change
class myImageFloder_8bit_t2t2(data.Dataset):
def __init__(self, left, cls, imgsize = 256, channels=4, aug=False, returnpath=False,
numsample=0):
self.left = left
self.cls = cls
if numsample>0:
self.left = left[:numsample]
self.cls = cls[:numsample]
self.aug = aug # augmentation for images
self.imgsize = imgsize # used for training and validation
self.channels = channels
self.returnpath = returnpath
def __getitem__(self, index):
left = self.left[index]
cls = self.cls[index]
# Read images
# left_img1 = tif.imread(left)[:, :, :self.channels] # TIF
cls_img = cv2.imread(cls, cv2.IMREAD_UNCHANGED) # PNG
left_img2 = tif.imread(left.replace('img1', 'img2'))[:, :, :self.channels] # TIF
left_img = np.concatenate((left_img2, left_img2), axis=2) # H W C
# Augmentation
if self.aug:
transformed = image_transformed_cls(image=left_img, mask=cls_img)
left_img = transformed["image"]
cls_img = transformed["mask"]
# scale images
left_img = torch.from_numpy(left_img).permute(2, 0, 1) # H W C ==> C H W
left_img = left_img.float()/255.0
# classification
cls_img = torch.from_numpy(cls_img).long()
if not self.returnpath:
return left_img, cls_img
else:
return left_img, cls_img, left
def __len__(self):
return len(self.left)
if __name__=="__main__":
#test
pass