From 5dacadeaa267bd1f968249fb8c876750163cc81e Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Tue, 26 Jan 2021 17:57:39 +0100
Subject: [PATCH] Implemented the computation of the spectral distribution of
the different classes.
---
pysegcnn/core/dataset.py | 45 +++++++++++++++++++++++++++++++++-------
1 file changed, 38 insertions(+), 7 deletions(-)
diff --git a/pysegcnn/core/dataset.py b/pysegcnn/core/dataset.py
index 5668a52..2366ecf 100644
--- a/pysegcnn/core/dataset.py
+++ b/pysegcnn/core/dataset.py
@@ -32,6 +32,7 @@ import pathlib
# externals
import numpy as np
+import pandas as pd
import torch
from torch.utils.data import Dataset
@@ -40,7 +41,8 @@ from pysegcnn.core.constants import (MultiSpectralSensor, Landsat8, Sentinel2,
Label, SparcsLabels, Cloud95Labels,
AlcdLabels)
from pysegcnn.core.utils import (img2np, is_divisible, tile_topleft_corner,
- parse_landsat_scene, parse_sentinel2_scene)
+ parse_landsat_scene, parse_sentinel2_scene,
+ array_replace)
# module level logger
LOGGER = logging.getLogger(__name__)
@@ -210,13 +212,12 @@ class ImageDataset(Dataset):
# always use the original dataset together with the augmentations
self.transforms = [None] + self.transforms
- # when padding, add a new "no data" label to the ground truth
- self.cval = self.label_class.No_data.id
+ # when padding, add a new "padded" label to the ground truth
if self.pad and sum(self.padding) > 0:
- LOGGER.info('Adding label "No data" with value={} to ground truth.'
+ self.cval = self.label_class.No_data.id
+ LOGGER.info('Padding to defined tile size. Padding value: {}.'
.format(self.cval))
else:
- # self._labels.pop(self.cval)
self.cval = 0
# remove labels to merge from dataset instance labels
@@ -629,6 +630,35 @@ class ImageDataset(Dataset):
"""
return torch.tensor(np.asarray(x).copy(), dtype=dtype)
+ def class_distribution(self):
+
+ # initialize class distribution dataframe
+ columns = [band.capitalize() for band in self.use_bands] + ['Class']
+ cls_df = pd.DataFrame(columns=columns)
+
+ # create the lookup table to replace the class identifiers by their
+ # corresponding labels
+ lookup = np.array(list({k: v['label'] for k, v in self.labels.items()}
+ .items())).astype(object)
+
+ # iterate over the samples of the dataset
+ for i in range(len(self)):
+ # get the data of the current sample
+ LOGGER.info('Sample: {}/{}'.format(i + 1, len(self)))
+ x, y = self[i]
+
+ # reshape the current sample
+ data = np.hstack([x.flatten(start_dim=1).T, np.expand_dims(
+ array_replace(y.flatten(), lookup), axis=1)])
+
+ # the pixels of the current sample to the dataframe
+ df = pd.DataFrame(data, columns=columns)
+
+ # update class distribution dataframe
+ cls_df = cls_df.append(df)
+
+ return cls_df
+
def __repr__(self):
"""Dataset representation.
@@ -659,8 +689,9 @@ class ImageDataset(Dataset):
# tiles
fs += '\n (tiles):\n '
fs += '- number of tiles per scene: {}\n '.format(self.tiles)
- fs += '- tile size: {}\n '.format((self.tile_size,
- self.tile_size))
+ fs += '- tile size: {}\n '.format(
+ 2 * (self.tile_size, ) if self.tile_size is not None else
+ (self.height, self.width))
fs += '- number of tiles: {}'.format(len(self.scenes))
# classes of interest
--
GitLab