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data.py
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from pathlib import Path
import numpy as np
import rasterio
import math
from enum import Enum, auto, unique
import torch
from torch.utils.data import Dataset
from utils import make_tuple
NUM_BANDS = 6
FINE_PREFIX = "LC08"
COARSE_PREFIX = "MOD09GA"
SCALE_FACTOR = 16
NODATA_VALUE = -9999
PATCH_SIZE = 256
@unique
class Mode(Enum):
TRAINING = auto()
VALIDATION = auto()
PREDICTION = auto()
def get_pair_path(directory: Path, mode: Mode):
# 将一组数据集按照规定的顺序组织好
# For Tianjin and Nebraska Datasets
dt_prev, dt_pred, dt_next = directory.stem.split('-')
orders = [
f'{FINE_PREFIX}*{dt_prev}*[!QA].tif',
f'{FINE_PREFIX}*{dt_next}*[!QA].tif',
f'{COARSE_PREFIX}.{dt_pred}*.tif',
f'{FINE_PREFIX}*{dt_pred}*[!QA].tif'
]
if mode == Mode.PREDICTION:
del orders[-1]
paths = []
for i in orders:
paths.append(next(directory.glob(i)))
# For CIA and LGC Datasets
# prev_label, pred_label, next_label = directory.name.split('-')
# prev_tokens, pred_tokens, next_tokens = prev_label.split('_'), pred_label.split('_'), next_label.split('_')
#
# def match(path: Path):
# return {
# prev_tokens[0] + prev_tokens[2] in path.stem and 'QA' not in path.stem: 0,
# next_tokens[0] + next_tokens[2] in path.stem and 'QA' not in path.stem: 1,
# pred_tokens[0] + pred_tokens[1] in path.stem: 2,
# pred_tokens[0] + pred_tokens[2] in path.stem and 'QA' not in path.stem: 3
# }
#
# paths = [None] * 4
# for f in Path(directory).glob('*.tif'):
# try:
# paths[match(f)[True]] = f.absolute().resolve()
# except KeyError:
# continue
#
# if mode is Mode.PREDICTION:
# del paths[-1]
return paths
# def random_mask(size):
# mask = np.ones((1, *size), np.int16)
# counts = random.randint(10, 30)
# for i in range(counts):
# width = random.randint(20, 100)
# height = random.randint(20, 100)
# x = random.randint(0, size[0] - 1)
# y = random.randint(0, size[1] - 1)
# mask[:, x: x + width, y: y + height] = 0
# return mask
#
#
# def load_image_pair(directory: Path, mode: Mode):
# # 按照一定顺序获取给定文件夹下的一组数据
# paths = get_pair_path(directory, mode)
# images = []
# for i in range(len(paths)):
# if i == 1 or i == 3:
# continue
# with rasterio.open(str(paths[i])) as ds:
# im = ds.read()
# mask = None
# if (i == 0 or i == 2) and mode == Mode.TRAINING:
# mask = random_mask(im.shape[1:])
# im *= mask
# im[im < 0] = 0
# images.append(im)
# if mask is not None:
# images.append(mask)
# return images
def load_image_pair(directory: Path, mode: Mode):
# 按照一定顺序获取给定文件夹下的一组数据
paths = get_pair_path(directory, mode)
images = []
for p in paths:
with rasterio.open(str(p)) as ds:
im = ds.read()
im[im < 0] = 0
images.append(im)
return images
class PatchSet(Dataset):
"""
每张图片分割成小块进行加载
"""
def __init__(self, image_dir, image_size, patch_size, patch_stride=None, mode=Mode.TRAINING):
super(PatchSet, self).__init__()
self.root_dir = image_dir
self.image_size = make_tuple(image_size)
self.patch_size = make_tuple(patch_size)
self.patch_stride = self.patch_size if patch_stride is None else make_tuple(patch_stride)
self.image_dirs = [p for p in self.root_dir.glob('*') if p.is_dir()]
self.num_im_pairs = len(self.image_dirs)
self.mode = mode
# 计算出图像进行分块以后的patches的数目
self.num_patches_x = math.ceil((self.image_size[0] - self.patch_size[0] + 1) / self.patch_stride[0])
self.num_patches_y = math.ceil((self.image_size[1] - self.patch_size[1] + 1) / self.patch_stride[1])
self.num_patches = self.num_im_pairs * self.num_patches_x * self.num_patches_y
def map_index(self, index):
# 将全局的index映射到具体的图像对文件夹索引(id_n)，图像裁剪的列号与行号(id_x, id_y)
id_n = index // (self.num_patches_x * self.num_patches_y)
residual = index % (self.num_patches_x * self.num_patches_y)
id_x = self.patch_stride[0] * (residual % self.num_patches_x)
id_y = self.patch_stride[1] * (residual // self.num_patches_x)
return id_n, id_x, id_y
def __getitem__(self, index):
id_n, id_x, id_y = self.map_index(index)
images = load_image_pair(self.image_dirs[id_n], self.mode)
patches = [None] * len(images)
for i in range(len(patches)):
im = images[i][:,
id_x: (id_x + self.patch_size[0]),
id_y: (id_y + self.patch_size[1])]
patches[i] = (lambda data: torch.from_numpy(data.astype(np.float32)))(im)
patches[i] = patches[i].mul_(0.0001)
del images[:]
del images
return patches
def __len__(self):
return self.num_patches