From 1e62b4abdd4c4df13bce5b98124b2d31e9fa84ba Mon Sep 17 00:00:00 2001
From: "Daniel.Frisinghelli" <daniel.frisinghelli@eurac.edu>
Date: Thu, 28 Jan 2021 17:39:19 +0100
Subject: [PATCH] Skip NoData class in class distributions.
---
pysegcnn/core/dataset.py | 26 +++-----------------------
1 file changed, 3 insertions(+), 23 deletions(-)
diff --git a/pysegcnn/core/dataset.py b/pysegcnn/core/dataset.py
index c04103c..b3678f4 100644
--- a/pysegcnn/core/dataset.py
+++ b/pysegcnn/core/dataset.py
@@ -633,17 +633,9 @@ class ImageDataset(Dataset):
def class_distribution(self):
# initialize dictionary of class spectral distribution
- cls_ds = {k: np.empty(shape=(0, len(self.use_bands))) for k, _ in
- self.labels.items()}
-
- # 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)
+ # exclude NoData class
+ cls_ds = {k: np.empty(shape=(0, len(self.use_bands))) for k, v in
+ self.labels.items() if v['label'] != 'No_data'}
# iterate over the samples of the dataset
for i in range(len(self)):
@@ -661,18 +653,6 @@ class ImageDataset(Dataset):
return cls_ds
- # 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.
--
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