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dataloader.py
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dataloader.py
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import torch
import numpy as np
import matplotlib.pyplot as plt
import os
import re
# class CroppedSegmentationPerTimeDataset(torch.utils.data.Dataset):
# '''
# This dataset contains images with the following shape: Cx64x64. Each image has been cropped out around the substation.
# Each timepoint has been stored as an individual image. However, since the mask is same for all of the individual timepoints, it is not saved again, rather it is referenced from original repo.
# Parameters:
# data_dir: Folder with the image and mask folders
# image_files: list of images to be included in the dataset
# in_channels: number of channels to be included per image. Max is 13
# geo_transforms: transformations like rotate, crop, flip, etc. Identical transformations are applied to the image and the mask.
# color_transforms: transformations like color jitter. These are not applied to the mask.
# use_timepoints: NOT USED-> need to removed
# normalizing_factor: factor to bring images to (0,1) scale.
# image_resize : Resizing operation on images
# mask_resize : Resizing operation on masks
# Returns:
# image,mask -> ((in_channels,64,64),(1,64,64))
# '''
# def __init__(self, data_dir, image_files, in_channels=13, geo_transforms=None, color_transforms= None, image_resize = None, mask_resize = None, use_timepoints=False, normalizing_factor=4000):
# self.data_dir = data_dir
# self.geo_transforms = geo_transforms
# self.color_transforms = color_transforms
# self.image_dir = os.path.join(data_dir, 'image_stack_cropped_per_time')
# self.mask_dir = os.path.join(data_dir, 'mask_cropped')
# self.image_filenames = image_files
# self.in_channels=in_channels
# self.use_timepoints = use_timepoints
# self.normalizing_factor = normalizing_factor
# self.image_resize = image_resize
# self.mask_resize = mask_resize
# def __getitem__(self, index):
# # load images and masks
# image_filename = self.image_filenames[index]
# image_path = os.path.join(self.image_dir, image_filename)
# mask_filename = image_filename[:image_filename.find('.npy') -2]+'.npz.npy'
# mask_path = os.path.join(self.mask_dir, mask_filename)
# image = np.load(image_path)
# image = image/self.normalizing_factor
# if self.in_channels==3:
# image = image[[3,2,1],:,:]
# else:
# image = image[:self.in_channels,:,:]
# mask = np.load(mask_path)
# image = torch.from_numpy(image) #inchannels,228,228
# mask = torch.from_numpy(mask).float().unsqueeze(0) #1x228x228
# if self.geo_transforms:
# combined = torch.cat((image,mask), 0)
# combined = self.geo_transforms(combined)
# image,mask = torch.split(combined, [image.shape[0],mask.shape[0]], 0)
# if self.color_transforms:
# image = self.color_transforms(image)
# if self.image_resize:
# image = self.image_resize(image)
# if self.mask_resize:
# mask = self.mask_resize(mask)
# image = torch.clip(image,0,1)
# return image, mask
# def __len__(self):
# return len(self.image_filenames)
# def plot(self):
# index = np.random.randint(0, self.__len__())
# image, mask = self.__getitem__(index)
# fig, axs = plt.subplots(1,2)
# axs[0].imshow(image.permute(1,2,0))
# axs[1].imshow(image.permute(1,2,0))
# axs[1].imshow(mask.permute(1,2,0), alpha=0.5, cmap='gray')
# class CroppedSegmentationDataset(torch.utils.data.Dataset):
# '''
# This dataset contains images with the following shape: TxCx64x64. Each image and mask has been cropped out around the substation.
# Parameters:
# data_dir: Folder with the image and mask folders
# image_files: list of images to be included in the dataset
# in_channels: number of channels to be included per image. Max is 13
# geo_transforms: transformations like rotate, crop, flip, etc. Identical transformations are applied to the image and the mask.
# color_transforms: transformations like color jitter. These are not applied to the mask.
# use_timepoints: if True, images from all timepoints are stacked along the channel. This results in images of the following shape: (T*CxHxW) Else, median across all timepoints is computed.
# normalizing_factor: factor to bring images to (0,1) scale.
# image_resize : Resizing operation on images
# mask_resize : Resizing operation on masks
# Returns:
# image,mask -> ((in_channels,64,64),(1,64,64))
# '''
# def __init__(self, data_dir, image_files, in_channels=3, geo_transforms=None, color_transforms= None, image_resize = None, mask_resize = None, use_timepoints=False, normalizing_factor=4000):
# self.data_dir = data_dir
# self.geo_transforms = geo_transforms
# self.color_transforms = color_transforms
# self.image_dir = os.path.join(data_dir, 'image_stack_cropped')
# self.mask_dir = os.path.join(data_dir, 'mask_cropped')
# self.image_filenames = image_files
# self.in_channels=in_channels
# self.use_timepoints = use_timepoints
# self.normalizing_factor = normalizing_factor
# self.image_resize = image_resize
# self.mask_resize = mask_resize
# def __getitem__(self, index):
# # load images and masks
# image_filename = self.image_filenames[index]
# image_path = os.path.join(self.image_dir, image_filename)
# mask_filename = image_filename
# mask_path = os.path.join(self.mask_dir, mask_filename)
# image = np.load(image_path)
# if self.in_channels==3:
# image = image[:,[3,2,1],:,:] #(t,3,h,w)
# else:
# image = image[:,:self.in_channels,:,:] #(t,in_channels,h,w)
# if self.use_timepoints:
# # t x 13 x h x w
# image = np.reshape(image, (-1, image.shape[2], image.shape[3])) #(t*in_channels,h,w)
# else:
# image = np.median(image, axis=0) #(in_channels,h,w)
# image = image/self.normalizing_factor
# mask = np.load(mask_path)
# image = torch.from_numpy(image) #3x228x228
# mask = torch.from_numpy(mask).float().unsqueeze(0) #1x228x228
# if self.geo_transforms:
# combined = torch.cat((image,mask), 0)
# combined = self.geo_transforms(combined)
# image,mask = torch.split(combined, [image.shape[0],mask.shape[0]], 0)
# if self.color_transforms:
# image = self.color_transforms(image)
# if self.image_resize:
# image = self.image_resize(image)
# if self.mask_resize:
# mask = self.mask_resize(mask)
# image = torch.clip(image,0,1)
# return image, mask
# def __len__(self):
# return len(self.image_filenames)
# def plot(self):
# index = np.random.randint(0, self.__len__())
# image, mask = self.__getitem__(index)
# fig, axs = plt.subplots(1,2)
# axs[0].imshow(image.permute(1,2,0))
# axs[1].imshow(image.permute(1,2,0))
# axs[1].imshow(mask.permute(1,2,0), alpha=0.5, cmap='gray')
class SubstationDataset(torch.utils.data.Dataset):
'''
This dataset contains images with the following shape: TxCx228x228.
Parameters:
data_dir: Folder with the image and mask folders
image_files: list of images to be included in the dataset
in_channels: number of channels to be included per image. Max is 13
geo_transforms: transformations like rotate, crop, flip, etc. Identical transformations are applied to the image and the mask.
color_transforms: transformations like color jitter. These are not applied to the mask.
image_resize : Resizing operation on images
mask_resize : Resizing operation on masks
use_timepoints: if True, images from all timepoints are stacked along the channel. This results in images of the following shape: (T*CxHxW) Else, median across all timepoints is computed.
normalizing_factor: factor to bring images to (0,1) scale.
mask_2d: if true, returns mask with dimension (2,h,w) else returns with dimension (1,h,w)
Returns:
image,mask -> ((in_channels,64,64),(1,64,64))
'''
def __init__(self, args, image_files, geo_transforms=None, color_transforms= None, image_resize = None, mask_resize = None,):
self.data_dir = args.data_dir
self.geo_transforms = geo_transforms
self.color_transforms = color_transforms
self.image_resize = image_resize
self.mask_resize = mask_resize
self.in_channels = args.in_channels
self.use_timepoints = args.use_timepoints
self.normalizing_type = args.normalizing_type
self.normalizing_factor = args.normalizing_factor
self.mask_2d = args.mask_2d
self.model_type = args.model_type
self.image_dir = os.path.join(os.path.join(args.data_dir,'substation'), 'image_stack')
self.mask_dir = os.path.join(os.path.join(args.data_dir,'substation'), 'mask')
self.image_filenames = image_files
self.args = args
def __getitem__(self, index):
image_filename = self.image_filenames[index]
image_path = os.path.join(self.image_dir, image_filename)
mask_filename = image_filename
mask_path = os.path.join(self.mask_dir, mask_filename)
image = np.load(image_path)['arr_0'] # t x 13 x h x w
#standardizing image
if self.normalizing_type=='percentile':
image = (image- self.normalizing_factor[:,0].reshape((-1,1,1)))/self.normalizing_factor[:,2].reshape((-1,1,1))
elif self.normalizing_type == 'zscore':
# means = np.array([1431, 1233, 1209, 1192, 1448, 2238, 2609, 2537, 2828, 884, 20, 2226, 1537]).reshape(-1, 1, 1)
# stds = np.array([157, 254, 290, 420, 363, 457, 575, 606, 630, 156, 3, 554, 523]).reshape(-1, 1, 1)
image = (image-self.args.means)/self.args.stds
else:
image = image/self.normalizing_factor
#clipping image to 0,1 range
image = np.clip(image, 0,1)
#selecting channels
if self.in_channels==3:
image = image[:,[3,2,1],:,:]
else:
if self.model_type =='swin':
image = image[:,[3,2,1,4,5,6,7,10,11],:,:] #swin only takes 9 channels
else:
image = image[:,:self.in_channels,:,:]
#handling multiple images across timepoints
if self.use_timepoints:
image = image[:4,:,:,:]
if self.args.timepoint_aggregation == 'concat':
image = np.reshape(image, (-1, image.shape[2], image.shape[3])) #(4*channels,h,w)
elif self.args.timepoint_aggregation == 'median':
image = np.median(image, axis=0)
else:
# image = np.median(image, axis=0)
# image = image[0]
if self.args.timepoint_aggregation == 'first':
image = image[0]
elif self.args.timepoint_aggregation == 'random':
image = image[np.random.randint(image.shape[0])]
mask = np.load(mask_path)['arr_0']
mask[mask != 3] = 0
mask[mask == 3] = 1
image = torch.from_numpy(image)
mask = torch.from_numpy(mask).float()
mask = mask.unsqueeze(dim=0)
if self.mask_2d:
mask_0 = 1.0-mask
mask = torch.concat([mask_0, mask], dim = 0)
# IMAGE AND MASK TRANSFORMATIONS
if self.geo_transforms:
combined = torch.cat((image,mask), 0)
combined = self.geo_transforms(combined)
image,mask = torch.split(combined, [image.shape[0],mask.shape[0]], 0)
if self.color_transforms:
num_timepoints = image.shape[0]//self.in_channels
for i in range(num_timepoints):
if self.in_channels >= 3:
image[i*self.in_channels:i*self.in_channels+3,:,:] = self.color_transforms(image[i*self.in_channels:i*self.in_channels+3,:,:])
else:
raise Exception("Can't apply color transformation. Make sure the correct input dimenions are used")
if self.image_resize:
image = self.image_resize(image)
if self.mask_resize:
mask = self.mask_resize(mask)
return image, mask
def __len__(self):
return len(self.image_filenames)
def plot(self):
index = np.random.randint(0, self.__len__())
image, mask = self.__getitem__(index)
fig, axs = plt.subplots(1,2, figsize = (15,15))
axs[0].imshow(image.permute(1,2,0))
axs[1].imshow(image.permute(1,2,0))
axs[1].imshow(mask.permute(1,2,0), alpha=0.5, cmap='gray')
class PhilEODataset(torch.utils.data.Dataset):
'''
This dataset contains images with the following shape: 3xCx224x224.
Parameters:
image_files: list of images to be included in the dataset
geo_transforms: transformations like rotate, crop, flip, etc. Identical transformations are applied to the image and the mask.
color_transforms: transformations like color jitter. These are not applied to the mask.
image_resize : Resizing operation on images
mask_resize : Resizing operation on masks
Returns:
image,mask -> ((in_channels,64,64),(1,64,64))
'''
def __init__(self, args, data_dir, image_files, geo_transforms=None, color_transforms= None, image_resize = None, mask_resize = None,):
self.data_dir = data_dir
self.geo_transforms = geo_transforms
self.color_transforms = color_transforms
self.image_resize = image_resize
self.mask_resize = mask_resize
self.in_channels = args.in_channels
self.use_timepoints = args.use_timepoints
self.normalizing_type = args.normalizing_type
self.normalizing_factor = args.normalizing_factor
self.mask_2d = args.mask_2d
self.model_type = args.model_type
self.image_dir = os.path.join(data_dir, 'images')
if args.task == 'building':
self.mask_dir = os.path.join(data_dir, 'building_mask')
elif args.task == 'roads':
self.mask_dir = os.path.join(data_dir, 'road_mask')
elif args.task == 'lc' :
self.mask_dir = os.path.join(data_dir, 'lc_mask')
self.image_filenames = image_files
self.args = args
def __getitem__(self, index):
image_filename = self.image_filenames[index]
image_path = os.path.join(self.image_dir, image_filename)
if self.args.task == 'building':
mask_filename = re.sub('s2','building_label',image_filename)
elif self.args.task == 'roads':
mask_filename = re.sub('s2','road_label',image_filename)
elif self.args.task == 'lc' :
mask_filename = re.sub('s2','lc_label',image_filename)
mask_path = os.path.join(self.mask_dir, mask_filename)
image = np.load(image_path)
image = image.transpose((0,3,1,2)) # t,c,h,w
#standardizing image
if self.normalizing_type=='percentile':
image = (image - self.normalizing_factor[:,0].reshape((-1,1,1)))/self.normalizing_factor[:,2].reshape((-1,1,1))
elif self.normalizing_type == 'zscore':
means = np.array([ 810., 1098., 1252., 2735., 1664., 2374., 2650., 2882., 2736., 2019.]).reshape(-1, 1, 1)
stds = np.array([304., 336., 432., 582., 405., 471., 519., 541., 505., 484.]).reshape(-1, 1, 1)
image = (image-means)/stds
else:
image = image/self.normalizing_factor
#clipping image to 0,1 range
image = np.clip(image, 0,1)
#selecting channels
if self.in_channels==3:
image = image[:,[2,1,0],:,:]
elif self.in_channels==13:
image = image[:,[0,0,1,2,4,5,6,3,7,7,8,8,9],:,:]
else:
if self.model_type =='swin':
image = image[:,[2,1,0,4,5,6,3,8,9],:,:] #swin only takes 9 channels
else:
image = image[:,:self.in_channels,:,:]
#handling multiple images across timepoints
if self.use_timepoints:
image = image[:3,:,:,:]
if self.args.timepoint_aggregation == 'concat':
image = np.reshape(image, (-1, image.shape[2], image.shape[3])) #(3*channels,h,w)
elif self.args.timepoint_aggregation == 'median':
image = np.median(image, axis=0)
else:
if self.args.timepoint_aggregation == 'first':
image = image[0]
elif self.args.timepoint_aggregation == 'random':
image = image[np.random.randint(image.shape[0])]
mask = np.load(mask_path)
mask = mask[0].transpose(2,0,1) #c,h,w
image = torch.from_numpy(image)
mask = torch.from_numpy(mask).float()
# mask = mask.unsqueeze(dim=0)
# if self.mask_2d:
# mask_0 = 1.0-mask
# mask = torch.concat([mask_0, mask], dim = 0)
# IMAGE AND MASK TRANSFORMATIONS
if self.geo_transforms:
combined = torch.cat((image,mask), 0)
combined = self.geo_transforms(combined)
image,mask = torch.split(combined, [image.shape[0],mask.shape[0]], 0)
if self.color_transforms:
num_timepoints = image.shape[0]//self.in_channels
for i in range(num_timepoints):
if self.in_channels >= 3:
image[i*self.in_channels:i*self.in_channels+3,:,:] = self.color_transforms(image[i*self.in_channels:i*self.in_channels+3,:,:])
else:
raise Exception("Can't apply color transformation. Make sure the correct input dimenions are used")
if self.image_resize:
image = self.image_resize(image)
if self.mask_resize:
mask = self.mask_resize(mask)
return image, mask
def __len__(self):
return len(self.image_filenames)
def plot(self):
index = np.random.randint(0, self.__len__())
image, mask = self.__getitem__(index)
fig, axs = plt.subplots(1,2, figsize = (15,15))
axs[0].imshow(image.permute(1,2,0))
axs[1].imshow(image.permute(1,2,0))
axs[1].imshow(mask.permute(1,2,0), alpha=0.5, cmap='gray')