From 39438a4d4bd5a5e2e11f2b1da261ba94752a9982 Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 14 Jul 2020 17:23:39 +0200
Subject: [PATCH] Added an option to chronologically sort a dataset
---
pytorch/dataset.py | 80 ++++++++++++++++++++++++++++++++++++----------
1 file changed, 63 insertions(+), 17 deletions(-)
diff --git a/pytorch/dataset.py b/pytorch/dataset.py
index 3218360..36a6456 100644
--- a/pytorch/dataset.py
+++ b/pytorch/dataset.py
@@ -33,6 +33,7 @@ sys.path.append('..')
from pytorch.constants import (Landsat8, Sentinel2, SparcsLabels,
Cloud95Labels, ProSnowLabels)
from pytorch.graphics import plot_sample
+from pytorch.utils import parse_landsat8_date, parse_sentinel2_date
# generic image dataset class
class ImageDataset(Dataset):
@@ -309,10 +310,18 @@ class ImageDataset(Dataset):
class StandardEoDataset(ImageDataset):
- def __init__(self, root_dir, use_bands, tile_size):
+ def __init__(self, root_dir, use_bands, tile_size, sort):
# initialize super class ImageDataset
super().__init__(root_dir, use_bands, tile_size)
+ # function that parses the date from a Landsat 8 scene id
+ self.date_parser = None
+
+ # whether to sort the list of samples:
+ # for time series data, set sort=True to obtain the scenes in
+ # chronological order
+ self.sort = sort
+
# returns the band number of a Landsat8 or Sentinel2 tif file
# x: path to a tif file
def get_band_number(self, path):
@@ -356,18 +365,22 @@ class StandardEoDataset(ImageDataset):
# to the tif files of each scene
# if the scenes are divided into tiles, each tile has its own entry
# with corresponding tile id
- def compose_scenes(self, pattern='*mask.png'):
+ def compose_scenes(self):
# list of all samples in the dataset
scenes = []
for scene in os.listdir(self.root):
+ # get the date of the current scene
+ date = self.date_parser(scene)
+
# list the spectral bands of the scene
bands = glob.glob(os.path.join(self.root, scene, '*B*.tif'))
# get the ground truth mask
try:
- gt = glob.glob(os.path.join(self.root, scene, pattern)).pop()
+ gt = glob.glob(
+ os.path.join(self.root, scene, '*mask.png')).pop()
except IndexError:
gt = None
@@ -380,18 +393,28 @@ class StandardEoDataset(ImageDataset):
# store tile number
data['tile'] = tile
+ # store date
+ data['date'] = date
+
# append to list
scenes.append(data)
+ # sort list of scenes in chronological order
+ if self.sort:
+ scenes.sort(key=lambda k: k['date'])
+
return scenes
# SparcsDataset class: inherits from the generic ImageDataset class
class SparcsDataset(StandardEoDataset):
def __init__(self, root_dir, use_bands=['red', 'green', 'blue'],
- tile_size=None):
+ tile_size=None, sort=False):
# initialize super class ImageDataset
- super().__init__(root_dir, use_bands, tile_size)
+ super().__init__(root_dir, use_bands, tile_size, sort)
+
+ # function that parses the date from a Landsat 8 scene id
+ self.date_parser = parse_landsat8_date
# list of all scenes in the root directory
# each scene is divided into tiles blocks
@@ -424,8 +447,11 @@ class SparcsDataset(StandardEoDataset):
class ProSnowDataset(StandardEoDataset):
- def __init__(self, root_dir, use_bands, tile_size):
- super().__init__(root_dir, use_bands, tile_size)
+ def __init__(self, root_dir, use_bands, tile_size, sort):
+ super().__init__(root_dir, use_bands, tile_size, sort)
+
+ # function that parses the date from a Sentinel 2 scene id
+ self.date_parser = parse_sentinel2_date
# list of samples in the dataset
self.scenes = self.compose_scenes()
@@ -452,13 +478,22 @@ class ProSnowDataset(StandardEoDataset):
class ProSnowGarmisch(ProSnowDataset):
- def __init__(self, root_dir, use_bands=[], tile_size=None):
- super().__init__(root_dir, use_bands, tile_size)
+ def __init__(self, root_dir, use_bands=[], tile_size=None, sort=True):
+ super().__init__(root_dir, use_bands, tile_size, sort)
def get_size(self):
return (615, 543)
+class ProSnowObergurgl(ProSnowDataset):
+
+ def __init__(self, root_dir, use_bands=[], tile_size=None, sort=True):
+ super().__init__(root_dir, use_bands, tile_size, sort)
+
+ def get_size(self):
+ return (310, 270)
+
+
class Cloud95Dataset(ImageDataset):
def __init__(self, root_dir, use_bands=[], tile_size=None, exclude=None):
@@ -570,17 +605,28 @@ if __name__ == '__main__':
cloud_path = os.path.join(wd, '_Datasets/Cloud95/Training')
# path to the ProSnow dataset
- prosnow_path = os.path.join(wd, '_Datasets/ProSnow/Garmisch')
+ prosnow_path = os.path.join(wd, '_Datasets/ProSnow/')
# the csv file containing the names of the informative patches
- patches = 'training_patches_95-cloud_nonempty.csv'
+ # patches = 'training_patches_95-cloud_nonempty.csv'
# instanciate the Cloud-95 dataset
- cloud_dataset = Cloud95Dataset(cloud_path, tile_size=192, exclude=patches)
+ # cloud_dataset = Cloud95Dataset(cloud_path,
+ # tile_size=192,
+ # exclude=patches)
# instanciate the SparcsDataset class
- sparcs_dataset = SparcsDataset(sparcs_path, tile_size=None,
- use_bands=['nir', 'red', 'green'])
-
- # instanciate the ProSnow class
- prosnow_dataset = ProSnowGarmisch(prosnow_path)
+ sparcs_dataset = SparcsDataset(sparcs_path,
+ tile_size=None,
+ use_bands=['nir', 'red', 'green'],
+ sort=False)
+
+ # instanciate the ProSnow datasets
+ garmisch = ProSnowGarmisch(os.path.join(prosnow_path, 'Garmisch'),
+ tile_size=None,
+ use_bands=['nir', 'red', 'green'],
+ sort=True)
+ obergurgl = ProSnowObergurgl(os.path.join(prosnow_path, 'Obergurgl'),
+ tile_size=None,
+ use_bands=['nir', 'red', 'green'],
+ sort=True)
--
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