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getdataset.py
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from torch.utils.data import Dataset
import numpy as np
import torch.nn as nn
import torch
import random
import os
import hdf5storage
import matplotlib.pyplot as plt
import cv2
from scipy import interpolate
import torch.nn.functional as F
import h5py
class TrainDataset_V1(Dataset):
def __init__(self, data_path, patch_size, arg=False):
self.arg = arg
self.data_path = data_path
self.patch_size = patch_size
data_list = os.listdir(data_path)
data_list.sort()
self.data_list = data_list
self.img_num = len(self.data_list)
def arguement(self, img, rotTimes, vFlip, hFlip):
# Random rotation
for j in range(rotTimes):
img = np.rot90(img.copy(), axes=(1, 2))
# Random vertical Flip
for j in range(vFlip):
img = img[:, :, ::-1].copy()
# Random horizontal Flip
for j in range(hFlip):
img = img[:, ::-1, :].copy()
return img
def __getitem__(self, idx):
f = h5py.File(self.data_path + self.data_list[idx], 'r')
hsi = f['hsi'][:]
f.close()
patch_size_h = self.patch_size[0]
patch_size_w = self.patch_size[1]
if self.arg:
rotTimes = random.randint(0, 3)
vFlip = random.randint(0, 1)
hFlip = random.randint(0, 1)
hsi = self.arguement(hsi, rotTimes, vFlip, hFlip)
random_h = random.randint(0,hsi.shape[1] - patch_size_h -1)
random_w = random.randint(0,hsi.shape[2] - patch_size_w -1)
output_hsi = hsi[:, random_h:random_h+patch_size_h, random_w:random_w+patch_size_w]
output_hsi = output_hsi.astype(np.float32)
output_hsi = output_hsi / output_hsi.max()
return np.ascontiguousarray(output_hsi)
def __len__(self):
return self.img_num
class ValidDataset_V1(Dataset):
def __init__(self, data_path, patch_size, arg=False):
self.arg = arg
self.data_paths = []
self.patch_size = patch_size
data_list = os.listdir(data_path)
data_list.sort()
for i in range(len(data_list)):
self.data_paths.append(data_path + data_list[i])
self.img_num = len(self.data_paths)
def arguement(self, img, rotTimes, vFlip, hFlip):
# Random rotation
for j in range(rotTimes):
img = np.rot90(img.copy(), axes=(1, 2))
# Random vertical Flip
for j in range(vFlip):
img = img[:, :, ::-1].copy()
# Random horizontal Flip
for j in range(hFlip):
img = img[:, ::-1, :].copy()
return img
def __getitem__(self, idx):
f = h5py.File(self.data_paths[idx], 'r')
hsi = f['hsi'][:]
f.close()
patch_size_h = self.patch_size[0]
patch_size_w = self.patch_size[1]
if self.arg:
rotTimes = random.randint(0, 3)
vFlip = random.randint(0, 1)
hFlip = random.randint(0, 1)
hsi = self.arguement(hsi, rotTimes, vFlip, hFlip)
random_h = random.randint(0, hsi.shape[1] - patch_size_h -1)
random_w = random.randint(0, hsi.shape[2] - patch_size_w -1)
output_hsi = hsi[:, random_h:random_h+patch_size_h, random_w:random_w+patch_size_w]
output_hsi = output_hsi.astype(np.float32)
output_hsi = output_hsi / output_hsi.max()
return np.ascontiguousarray(output_hsi)
def __len__(self):
return self.img_num
class TrainDataset_V2(Dataset):
def __init__(self, data_path, patch_size, arg=False):
self.arg = arg
self.data_path = data_path
self.patch_size = patch_size
self.select_index = np.concatenate((np.arange(0,61,1), np.arange(62, 132, 2)))
data_list = os.listdir(data_path)
data_list.sort()
self.data_list = data_list
self.img_num = len(self.data_list)
def arguement(self, img, rotTimes, vFlip, hFlip):
# Random rotation
for j in range(rotTimes):
img = np.rot90(img.copy(), axes=(1, 2))
# Random vertical Flip
for j in range(vFlip):
img = img[:, :, ::-1].copy()
# Random horizontal Flip
for j in range(hFlip):
img = img[:, ::-1, :].copy()
return img
def __getitem__(self, idx):
f = h5py.File(self.data_path + self.data_list[idx], 'r')
hsi = f['hsi'][:]
hsi = hsi[self.select_index, :, :]
f.close()
patch_size_h = self.patch_size[0]
patch_size_w = self.patch_size[1]
if self.arg:
rotTimes = random.randint(0, 3)
vFlip = random.randint(0, 1)
hFlip = random.randint(0, 1)
hsi = self.arguement(hsi, rotTimes, vFlip, hFlip)
random_h = random.randint(0,hsi.shape[1] - patch_size_h -1)
random_w = random.randint(0,hsi.shape[2] - patch_size_w -1)
output_hsi = hsi[:, random_h:random_h+patch_size_h, random_w:random_w+patch_size_w]
output_hsi = output_hsi.astype(np.float32)
output_hsi = output_hsi / output_hsi.max()
return np.ascontiguousarray(output_hsi)
def __len__(self):
return self.img_num
class ValidDataset_V2(Dataset):
def __init__(self, data_path, patch_size, arg=False):
self.arg = arg
self.data_paths = []
self.patch_size = patch_size
self.select_index = np.concatenate((np.arange(0,61,1), np.arange(62, 132, 2)))
data_list = os.listdir(data_path)
data_list.sort()
for i in range(len(data_list)):
self.data_paths.append(data_path + data_list[i])
self.img_num = len(self.data_paths)
def arguement(self, img, rotTimes, vFlip, hFlip):
# Random rotation
for j in range(rotTimes):
img = np.rot90(img.copy(), axes=(1, 2))
# Random vertical Flip
for j in range(vFlip):
img = img[:, :, ::-1].copy()
# Random horizontal Flip
for j in range(hFlip):
img = img[:, ::-1, :].copy()
return img
def __getitem__(self, idx):
f = h5py.File(self.data_paths[idx], 'r')
hsi = f['hsi'][:]
hsi = hsi[self.select_index, :, :]
f.close()
patch_size_h = self.patch_size[0]
patch_size_w = self.patch_size[1]
if self.arg:
rotTimes = random.randint(0, 3)
vFlip = random.randint(0, 1)
hFlip = random.randint(0, 1)
hsi = self.arguement(hsi, rotTimes, vFlip, hFlip)
random_h = random.randint(0, hsi.shape[1] - patch_size_h -1)
random_w = random.randint(0, hsi.shape[2] - patch_size_w -1)
output_hsi = hsi[:, random_h:random_h+patch_size_h, random_w:random_w+patch_size_w]
output_hsi = output_hsi.astype(np.float32)
output_hsi = output_hsi / output_hsi.max()
return np.ascontiguousarray(output_hsi)
def __len__(self):
return self.img_num
class TestDataset_MOS(Dataset):
def __init__(self, data_path, data_list, start_dir, image_size, arg=False):
self.arg = arg
self.data_path = data_path
self.start_dir = start_dir
self.image_size = image_size
self.data_list = data_list
self.MOS_list = []
for i in range(len(data_list)):
bmp = cv2.imread(self.data_path + self.data_list[i])[:, :, 0]
bmp = bmp[self.start_dir[0]:self.start_dir[0]+self.image_size[0], self.start_dir[1]:self.start_dir[1] + self.image_size[1]]
bmp = bmp / bmp.max()
bmp = bmp.astype(np.float32)
mos = np.expand_dims(bmp, axis=0)
self.MOS_list.append(mos)
self.img_num = len(self.data_list)
def __getitem__(self, idx):
mos_name = self.data_list[idx]
mos = self.MOS_list[idx]
return np.ascontiguousarray(mos), mos_name
def __len__(self):
return self.img_num